{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "396ee321-ba2e-41de-893a-4b0b8c4fdd9b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from echoNetDynamic.utils import loadvideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "c5364c7c-1af8-40c4-995b-738607cf433b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.io\n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from monai.handlers.utils import from_engine\n",
    "# https://docs.monai.io/en/stable/networks.html#nets\n",
    "from monai.networks.nets import UNet,AttentionUnet, DynUNet, SegResNet, VNet, SegResNetVAE, UNETR\n",
    "from monai.networks.layers import Norm\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.losses import DiceLoss\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.data import CacheDataset, DataLoader, Dataset, decollate_batch\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import aim\n",
    "from aim.pytorch import track_gradients_dists, track_params_dists\n",
    "import matplotlib.pyplot as plt\n",
    "import tempfile\n",
    "import shutil\n",
    "import os\n",
    "import torch\n",
    "import random\n",
    "\n",
    "np.set_printoptions(threshold=np.inf)\n",
    "random.seed(7777)\n",
    "np.random.seed(7777)\n",
    "\n",
    "NUM_PREFETCH = 10\n",
    "RANDOM_SEED = 123\n",
    "\n",
    "root_dir = '/mnt/datawow/lyq/dataset/HMC-QU'\n",
    "img_dir = os.path.join(root_dir,'A4C/*.avi')\n",
    "mask_dir = img_dir.replace('A4C','LV Ground-truth Segmentation Masks').replace('avi','mat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce6dd9f4-c3bf-4681-b73b-d87fb531a8b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "masks = glob(mask_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "31dce16e-944d-4e3d-a1d7-9e782357fdeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "images = [i.replace('LV Ground-truth Segmentation Masks','A4C').replace('Mask_','').replace('mat','avi') for i in masks]\n",
    "data_dicts = [{\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(images, masks)]\n",
    "train_num = int(len(data_dicts)*0.7)\n",
    "train_files, val_files = data_dicts[:-train_num], data_dicts[-train_num:]\n",
    "\n",
    "all_keys = ['image', 'label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "476e6618-0441-4cf8-aded-4748653db67b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 36, 422, 636) [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255]\n",
      "(3, 37, 422, 636) [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255]\n",
      "(3, 42, 422, 636) [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255]\n",
      "(3, 51, 434, 636) [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255]\n",
      "(3, 49, 434, 636) [  0   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17\n",
      "  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35\n",
      "  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53\n",
      "  54  55  56  57  58  59  60  61  62  63  64  65  66  67  68  69  70  71\n",
      "  72  73  74  75  76  77  78  79  80  81  82  83  84  85  86  87  88  89\n",
      "  90  91  92  93  94  95  96  97  98  99 100 101 102 103 104 105 106 107\n",
      " 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125\n",
      " 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143\n",
      " 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161\n",
      " 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179\n",
      " 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n",
      " 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215\n",
      " 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233\n",
      " 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251\n",
      " 252 253 254 255]\n"
     ]
    }
   ],
   "source": [
    "for i in images[:5]:\n",
    "    arr = loadvideo(i)\n",
    "    print(arr.shape, np.unique(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0071f92f-189e-4a20-a13c-b8dc179d488c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(arr[0,18:35,0,0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b7549b48-5507-4bcd-ae53-42bc16b4d274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/datawow/lyq/dataset/HMC-QU/A4C/ES000116 _4CH_2.avi'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "images[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "dda32870-3c8d-4697-a844-c75e10f13a67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/datawow/lyq/dataset/HMC-QU/LV Ground-truth Segmentation Masks/Mask_ES000116 _4CH_2.mat'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "masks[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "4aa34cf7-0e0b-4f62-a0b5-734265f58dd9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/mnt/datawow/lyq/dataset/HMC-QU/LV Ground-truth Segmentation Masks/Mask_ES000116 _4CH_2.mat (18, 224, 224) (array([0., 1.], dtype=float32), array([870085,  33083]))\n",
      "/mnt/datawow/lyq/dataset/HMC-QU/LV Ground-truth Segmentation Masks/Mask_ES00014 _4CH_2.mat (18, 224, 224) (array([0., 1.], dtype=float32), array([860559,  42609]))\n",
      "/mnt/datawow/lyq/dataset/HMC-QU/LV Ground-truth Segmentation Masks/Mask_ES00038 _4CH_2.mat (21, 224, 224) (array([0., 1.], dtype=float32), array([1001492,   52204]))\n",
      "/mnt/datawow/lyq/dataset/HMC-QU/LV Ground-truth Segmentation Masks/Mask_ES000131_4CH_1.mat (25, 224, 224) (array([0., 1.], dtype=float32), array([1208142,   46258]))\n",
      "/mnt/datawow/lyq/dataset/HMC-QU/LV Ground-truth Segmentation Masks/Mask_ES00057 N_4CH_1.mat (24, 224, 224) (array([0., 1.], dtype=float32), array([1174334,   29890]))\n"
     ]
    }
   ],
   "source": [
    "for i in masks[:5]:\n",
    "    mat_data = scipy.io.loadmat(i)\n",
    "    arr = mat_data['predicted']\n",
    "    print(i, arr.shape, np.unique(arr, return_counts=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "fdca10b5-b112-4984-a34d-6a5df62369b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(os.path.join(root_dir,'A4C.xlsx'),skiprows=[0])\n",
    "df.columns = ['id', 'SEG3', 'SEG9', 'SEG14', 'SEG16', 'SEG12', 'SEG6',\n",
    "       'Reference Frame', 'End of Cycle', 'Available']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0bc94be6-d833-47d3-9a61-a939aac042e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>SEG3</th>\n",
       "      <th>SEG9</th>\n",
       "      <th>SEG14</th>\n",
       "      <th>SEG16</th>\n",
       "      <th>SEG12</th>\n",
       "      <th>SEG6</th>\n",
       "      <th>Reference Frame</th>\n",
       "      <th>End of Cycle</th>\n",
       "      <th>Available</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ES000116 _4CH_2</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>ü</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id SEG3 SEG9 SEG14 SEG16 SEG12 SEG6  Reference Frame  \\\n",
       "14  ES000116 _4CH_2   MI   MI    MI    MI    MI   MI               18   \n",
       "\n",
       "    End of Cycle Available  \n",
       "14            35         ü  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['id']=='ES000116 _4CH_2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "f95b6f00-c7a8-468e-ad43-94b5ff880991",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Reference Frame</th>\n",
       "      <th>End of Cycle</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3.580247</td>\n",
       "      <td>24.685185</td>\n",
       "      <td>21.104938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>9.300857</td>\n",
       "      <td>10.967752</td>\n",
       "      <td>5.900424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>18.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>21.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>25.750000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>72.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Reference Frame  End of Cycle        diff\n",
       "count       162.000000    162.000000  162.000000\n",
       "mean          3.580247     24.685185   21.104938\n",
       "std           9.300857     10.967752    5.900424\n",
       "min           1.000000     13.000000   12.000000\n",
       "25%           1.000000     19.000000   18.000000\n",
       "50%           1.000000     22.000000   21.000000\n",
       "75%           1.000000     25.750000   24.000000\n",
       "max          60.000000     73.000000   72.000000"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "8c4372dc-9c32-4d85-ac65-f42ecfdeaf7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['diff'] = df['End of Cycle']-df['Reference Frame']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "800dbdb2-1a3e-441d-bf89-01c93d88df2e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['ES0001 _4CH_1', 'ES00010 _4CH_1', 'ES000102 _4CH_1',\n",
       "       'ES000103 _4CH_2', 'ES000105 _4CH_1', 'ES000106 _4CH_1',\n",
       "       'ES000107 _4CH_1', 'ES000108 _4CH_1', 'ES000109 _4CH_2',\n",
       "       'ES00011 _4CH_1', 'ES000112 _4CH_3', 'ES000113 _4CH_2',\n",
       "       'ES000114 _4CH_1', 'ES000115 _4CH_1', 'ES000116 _4CH_2',\n",
       "       'ES000117 _4CH_1', 'ES000120_4CH_1', 'ES000121_4CH_1',\n",
       "       'ES000122_4CH_3', 'ES000123_4CH_1', 'ES000124_4CH_1',\n",
       "       'ES000125_4CH_1', 'ES000126_4CH_2', 'ES000128_4CH_1',\n",
       "       'ES000129_4CH_1', 'ES00013 _4CH_1', 'ES000131_4CH_1',\n",
       "       'ES000134_4CH_1', 'ES000135_4CH_1', 'ES000138_4CH_1',\n",
       "       'ES000139_4CH_1', 'ES00014 _4CH_2', 'ES000140_4CH_2',\n",
       "       'ES000142_4CH_1', 'ES000143_4CH_1', 'ES000144_4CH_1',\n",
       "       'ES000147_4CH_2', 'ES00015 _4CH_1', 'ES000150_4CH_2',\n",
       "       'ES000151_4CH_3', 'ES000152_4CH_2', 'ES00016 _4CH_3',\n",
       "       'ES00017 _4CH_2', 'ES00018 _4CH_1', 'ES00019 _4CH_1',\n",
       "       'ES0002 _4CH_1', 'ES00020 _4CH_1', 'ES00021 _4CH_3',\n",
       "       'ES00023 _4CH_1', 'ES00024 _4CH_2', 'ES00027 _4CH_2',\n",
       "       'ES00028 _4CH_5', 'ES00029 _4CH_3', 'ES0003 _4CH_1',\n",
       "       'ES00030 _4CH_2', 'ES00035 _4CH_1', 'ES00038 _4CH_2',\n",
       "       'ES00039 _4CH_1', 'ES0004 _4CH_1', 'ES00040 _4CH_1',\n",
       "       'ES00041 _4CH_2', 'ES00046 _4CH_1', 'ES00047 _4CH_2',\n",
       "       'ES00048 _4CH_1', 'ES00049 _4CH_4', 'ES0005 _4CH_1',\n",
       "       'ES00050 _4CH_3', 'ES0008 _4CH_1', 'ES000155 n_4CH_1',\n",
       "       'ES000159 n_4CH_1', 'ES000161 n_4CH_1', 'ES000164 n_4CH_1',\n",
       "       'ES000166 n_4CH_1', 'ES000167 n_4CH_1', 'ES000170 n_4CH_1',\n",
       "       'ES000176 n_4CH_1', 'ES000178 n_4CH_2', 'ES000179 n_4CH_1',\n",
       "       'ES000182 n_4CH_1', 'ES000183 n_4CH_2', 'ES000186 n_4CH_2',\n",
       "       'ES000187 n_4CH_2', 'ES000188 n_4CH_2', 'ES000189 n_4CH_1',\n",
       "       'ES000196 n_4CH_1', 'ES000197 n_4CH_1', 'ES000199 n_4CH_1',\n",
       "       'ES000200 n_4CH_1', 'ES000201 n_4CH_1', 'ES00051 N_4CH_2',\n",
       "       'ES00052 N_4CH_2', 'ES00057 N_4CH_1', 'ES00067 N_4CH_1',\n",
       "       'ES00068 N_4CH_1', 'ES00072 N_4CH_1', 'ES00074 N_4CH_3',\n",
       "       'ES00075 N_4CH_2', 'ES00076 N_4CH_1', 'ES00079 N_4CH_1',\n",
       "       'ES00080 N_4CH_3', 'ES00081 N_4CH_2', 'ES00082 N_4CH_2',\n",
       "       'ES00083 N_4CH_2', 'ES00090 N_4CH_1', 'ES00092 N_4CH_1',\n",
       "       'ES00094 N_4CH_1', 'ES00098 N_4CH_1', 'ES000130_4CH_1',\n",
       "       'ES00026 _4CH_1', 'ES000101 _4CH_1', 'ES000104 _4CH_1',\n",
       "       'ES000111 _4CH_1', 'ES000119 _4CH_2', 'ES00012 _4CH_2',\n",
       "       'ES000132_4CH_1', 'ES000133_4CH_1', 'ES000137_4CH_1',\n",
       "       'ES000148_4CH_1', 'ES00022 _4CH_1', 'ES00025 _4CH_5',\n",
       "       'ES00031 _4CH_1', 'ES00032 _4CH_2', 'ES00033 _4CH_1',\n",
       "       'ES00034 _4CH_5', 'ES00037 _4CH_4', 'ES00042 _4CH_2',\n",
       "       'ES00043 _4CH_4', 'ES00044 _4CH_4', 'ES00045 _4CH_6',\n",
       "       'ES0006 _4CH_1', 'ES0007 _4CH_1', 'ES0009 _4CH_1',\n",
       "       'ES000100 N_4CH_1', 'ES000153 n_4CH_3', 'ES000158 n_4CH_2',\n",
       "       'ES000162 n_4CH_1', 'ES000171 n_4CH_1', 'ES000174 n_4CH_1',\n",
       "       'ES000191 n_4CH_1', 'ES00053 N_4CH_3', 'ES00055 N_4CH_1',\n",
       "       'ES00056 N_4CH_3', 'ES00058 N_4CH_2', 'ES00059 N_4CH_2',\n",
       "       'ES00061 N_4CH_1', 'ES00062 N_4CH_1', 'ES00065 N_4CH_2',\n",
       "       'ES00071 N_4CH_3', 'ES00077 N_4CH_1', 'ES00078 N_4CH_1',\n",
       "       'ES00084 N_4CH_3', 'ES00085 N_4CH_2', 'ES00086 N_4CH_2',\n",
       "       'ES00087 N_4CH_2', 'ES00088 N_4CH_1', 'ES00089 N_4CH_2',\n",
       "       'ES00091 N_4CH_2', 'ES00093 N_4CH_1', 'ES00095 N_4CH_1',\n",
       "       'ES00096 N_4CH_1', 'ES00097 N_4CH_1', 'ES00099 N_4CH_2'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "6cccf09a-38ba-47b2-88b2-55bab1ab1f69",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-20 17:41:50.443613: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-06-20 17:41:50.464822: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-20 17:41:50.973743: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    }
   ],
   "source": [
    "from monai.transforms import (\n",
    "    AsDiscrete,\n",
    "    AsDiscreted,\n",
    "        EnsureChannelFirstd,\n",
    "    Compose,\n",
    "    LoadImaged,\n",
    "    RandSpatialCropd,\n",
    "    CenterSpatialCropd,\n",
    "    EnsureChannelFirstd,\n",
    "    RandFlipd,\n",
    "    Resized,\n",
    "    ScaleIntensityRangePercentilesd,\n",
    "    NormalizeIntensityd,\n",
    "    Identity,\n",
    "    EnsureTyped,\n",
    "MapTransform\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "cc3180d2-30ce-4351-8419-a0691a3b52fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "spatial_size = (-1,224,224)\n",
    "crop_size = (8,128,128)\n",
    "\n",
    "class LoadHMCImage(MapTransform):\n",
    "    def __init__(self, keys, info_df, allow_missing_keys=False):\n",
    "        super().__init__(keys, allow_missing_keys)\n",
    "        self.info_df = info_df\n",
    "    \n",
    "    def get_frame(self, id_value):\n",
    "        filtered_row = self.info_df[self.info_df['id'] == id_value]\n",
    "        start = filtered_row['Reference Frame'].values[0]\n",
    "        end = filtered_row['End of Cycle'].values[0]\n",
    "        return start, end\n",
    "        \n",
    "    def __call__(self, data):\n",
    "        d = dict(data)\n",
    "        for key in self.keys:\n",
    "            path = d[key]\n",
    "            if path.split('.')[-1]=='avi':\n",
    "                arr = loadvideo(path)\n",
    "                sid = path.split('/')[-1].replace('.avi','')\n",
    "            else:\n",
    "                mat_data = scipy.io.loadmat(path)\n",
    "                arr = mat_data['predicted']\n",
    "                sid = path.split('/')[-1].replace('.mat','').replace('Mask_','')\n",
    "            start, end = self.get_frame(sid)\n",
    "            shape_origin = arr.shape\n",
    "            if path.split('.')[-1]=='avi':\n",
    "                arr = arr[:,start-1:end,:,:]\n",
    "            d[key] = arr\n",
    "            # d['name'] = f'{key}_{sid}'\n",
    "            # print(sid, key, start, end, shape_origin, arr.shape)\n",
    "        return d\n",
    "\n",
    "train_transforms = Compose([\n",
    "        LoadHMCImage(keys=[\"image\", \"label\"],info_df=df),\n",
    "        EnsureChannelFirstd(keys=['label'],channel_dim='no_channel' ),\n",
    "        Resized(keys=['image'], spatial_size=spatial_size, allow_missing_keys=True, mode='nearest'),\n",
    "        RandSpatialCropd(all_keys, crop_size, random_size=False, allow_missing_keys=True),\n",
    "        #ScaleIntensityRangePercentilesd(keys=all_keys, lower=5, upper=95, b_min=0., b_max=1., allow_missing_keys=True) if len(all_keys)>0 else Identity(),\n",
    "        #NormalizeIntensityd(keys=all_keys, subtrahend=0.5, divisor=0.5),\n",
    "        EnsureTyped(keys=all_keys, allow_missing_keys=True),\n",
    "    ])\n",
    "\n",
    "val_transforms = Compose([\n",
    "        LoadHMCImage(keys=[\"image\", \"label\"],info_df=df),\n",
    "        EnsureChannelFirstd(keys=['label'],channel_dim='no_channel' ),\n",
    "        Resized(keys=['image'], spatial_size=spatial_size, allow_missing_keys=True, mode='nearest'),\n",
    "        RandSpatialCropd(all_keys, crop_size, random_size=False, allow_missing_keys=True),\n",
    "        #ScaleIntensityRangePercentilesd(keys=all_keys, lower=5, upper=95, b_min=0., b_max=1., allow_missing_keys=True) if len(all_keys)>0 else Identity(),\n",
    "        #NormalizeIntensityd(keys=all_keys, subtrahend=0.5, divisor=0.5),\n",
    "        EnsureTyped(keys=all_keys, allow_missing_keys=True),\n",
    "    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "7ed6cd9c-af83-437a-94c2-d75301f0a87c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>SEG3</th>\n",
       "      <th>SEG9</th>\n",
       "      <th>SEG14</th>\n",
       "      <th>SEG16</th>\n",
       "      <th>SEG12</th>\n",
       "      <th>SEG6</th>\n",
       "      <th>Reference Frame</th>\n",
       "      <th>End of Cycle</th>\n",
       "      <th>Available</th>\n",
       "      <th>diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ES000116 _4CH_2</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>MI</td>\n",
       "      <td>18</td>\n",
       "      <td>35</td>\n",
       "      <td>ü</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id SEG3 SEG9 SEG14 SEG16 SEG12 SEG6  Reference Frame  \\\n",
       "14  ES000116 _4CH_2   MI   MI    MI    MI    MI   MI               18   \n",
       "\n",
       "    End of Cycle Available  diff  \n",
       "14            35         ü    17  "
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['id']=='ES000116 _4CH_2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "9792728b-58fc-437d-b8b7-8159cdc14e74",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train num: 17\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([232409,  29735]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([230100,  32044]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([250275,  11869]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([233143,  29001]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([237402,  24742]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([228761,  33383]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([236715,  25429]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([232397,  29747]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([237987,  24157]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([250606,  11538]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([252613,   9531]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([240812,  21332]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([232460,  29684]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([238649,  23495]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([243160,  18984]))\n",
      "torch.Size([2, 3, 8, 128, 128]) torch.Size([2, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([228843,  33301]))\n",
      "torch.Size([1, 3, 8, 128, 128]) torch.Size([1, 1, 8, 128, 128]) (array([0., 1.], dtype=float32), array([113253,  17819]))\n"
     ]
    }
   ],
   "source": [
    "from monai.data import CacheDataset, DataLoader, Dataset, decollate_batch\n",
    "\n",
    "# train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0, num_workers=4)\n",
    "train_ds = Dataset(data=train_files, transform=train_transforms)\n",
    "\n",
    "# use batch_size=2 to load images and use RandCropByPosNegLabeld \n",
    "# to generate 2 x 4 images for network training\n",
    "train_loader = DataLoader(train_ds, batch_size=2, shuffle=False, num_workers=1)\n",
    "\n",
    "# val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0, num_workers=4)\n",
    "val_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=1, shuffle=False, num_workers=4)\n",
    "\n",
    "step=0\n",
    "print('train num:',len(train_loader))\n",
    "for batch_data in train_loader:\n",
    "    inputs, labels = (\n",
    "        batch_data[\"image\"],\n",
    "        batch_data[\"label\"],\n",
    "    )\n",
    "    print( inputs.shape, labels.shape, np.unique(labels,return_counts=True))\n",
    "    step+=1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "0dbd50bc-c92d-462b-bf6c-7ebd6ee5d653",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1071048,   32824]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1127070,   77154]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([950079,  53441]))\n",
      "torch.Size([1, 3, 19, 224, 224]) torch.Size([1, 1, 19, 224, 224]) (array([0., 1.], dtype=float32), array([914694,  38650]))\n",
      "torch.Size([1, 3, 19, 224, 224]) torch.Size([1, 1, 19, 224, 224]) (array([0., 1.], dtype=float32), array([916060,  37284]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1140711,   63513]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1159542,   44682]))\n",
      "torch.Size([1, 3, 25, 224, 224]) torch.Size([1, 1, 25, 224, 224]) (array([0., 1.], dtype=float32), array([1202645,   51755]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1051544,   52328]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([956715,  46805]))\n",
      "torch.Size([1, 3, 21, 224, 224]) torch.Size([1, 1, 21, 224, 224]) (array([0., 1.], dtype=float32), array([1007036,   46660]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1071288,   32584]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([957528,  45992]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1057464,   46408]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([813413,  39579]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1259554,   45022]))\n",
      "torch.Size([1, 3, 29, 224, 224]) torch.Size([1, 1, 29, 224, 224]) (array([0., 1.], dtype=float32), array([1406161,   48943]))\n",
      "torch.Size([1, 3, 23, 224, 224]) torch.Size([1, 1, 23, 224, 224]) (array([0., 1.], dtype=float32), array([1096420,   57628]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([811150,  41842]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1242548,   62028]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1242490,   62086]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1167115,   37109]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1250144,   54432]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1263262,   41314]))\n",
      "torch.Size([1, 3, 23, 224, 224]) torch.Size([1, 1, 23, 224, 224]) (array([0., 1.], dtype=float32), array([1093058,   60990]))\n",
      "torch.Size([1, 3, 25, 224, 224]) torch.Size([1, 1, 25, 224, 224]) (array([0., 1.], dtype=float32), array([1203017,   51383]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([956445,  47075]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1266143,   38433]))\n",
      "torch.Size([1, 3, 16, 224, 224]) torch.Size([1, 1, 16, 224, 224]) (array([0., 1.], dtype=float32), array([767303,  35513]))\n",
      "torch.Size([1, 3, 16, 224, 224]) torch.Size([1, 1, 16, 224, 224]) (array([0., 1.], dtype=float32), array([773057,  29759]))\n",
      "torch.Size([1, 3, 18, 224, 224]) torch.Size([1, 1, 18, 224, 224]) (array([0., 1.], dtype=float32), array([868297,  34871]))\n",
      "torch.Size([1, 3, 28, 224, 224]) torch.Size([1, 1, 28, 224, 224]) (array([0., 1.], dtype=float32), array([1357415,   47513]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1060111,   43761]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([829160,  23832]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1257972,   46604]))\n",
      "torch.Size([1, 3, 23, 224, 224]) torch.Size([1, 1, 23, 224, 224]) (array([0., 1.], dtype=float32), array([1103240,   50808]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1153875,   50349]))\n",
      "torch.Size([1, 3, 19, 224, 224]) torch.Size([1, 1, 19, 224, 224]) (array([0., 1.], dtype=float32), array([911091,  42253]))\n",
      "torch.Size([1, 3, 16, 224, 224]) torch.Size([1, 1, 16, 224, 224]) (array([0., 1.], dtype=float32), array([767471,  35345]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([959500,  44020]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1142420,   61804]))\n",
      "torch.Size([1, 3, 19, 224, 224]) torch.Size([1, 1, 19, 224, 224]) (array([0., 1.], dtype=float32), array([908809,  44535]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([813727,  39265]))\n",
      "torch.Size([1, 3, 16, 224, 224]) torch.Size([1, 1, 16, 224, 224]) (array([0., 1.], dtype=float32), array([767129,  35687]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1049807,   54065]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([819862,  33130]))\n",
      "torch.Size([1, 3, 25, 224, 224]) torch.Size([1, 1, 25, 224, 224]) (array([0., 1.], dtype=float32), array([1196077,   58323]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1164701,   39523]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([802647,  50345]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1156103,   48121]))\n",
      "torch.Size([1, 3, 26, 224, 224]) torch.Size([1, 1, 26, 224, 224]) (array([0., 1.], dtype=float32), array([1250679,   53897]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1069132,   34740]))\n",
      "torch.Size([1, 3, 27, 224, 224]) torch.Size([1, 1, 27, 224, 224]) (array([0., 1.], dtype=float32), array([1285884,   68868]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1056403,   47469]))\n",
      "torch.Size([1, 3, 14, 224, 224]) torch.Size([1, 1, 14, 224, 224]) (array([0., 1.], dtype=float32), array([671763,  30701]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1156103,   48121]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([950928,  52592]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([959430,  44090]))\n",
      "torch.Size([1, 3, 16, 224, 224]) torch.Size([1, 1, 16, 224, 224]) (array([0., 1.], dtype=float32), array([776760,  26056]))\n",
      "torch.Size([1, 3, 21, 224, 224]) torch.Size([1, 1, 21, 224, 224]) (array([0., 1.], dtype=float32), array([1019646,   34050]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1056246,   47626]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([941546,  61974]))\n",
      "torch.Size([1, 3, 17, 224, 224]) torch.Size([1, 1, 17, 224, 224]) (array([0., 1.], dtype=float32), array([813957,  39035]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1153972,   50252]))\n",
      "torch.Size([1, 3, 28, 224, 224]) torch.Size([1, 1, 28, 224, 224]) (array([0., 1.], dtype=float32), array([1359062,   45866]))\n",
      "torch.Size([1, 3, 28, 224, 224]) torch.Size([1, 1, 28, 224, 224]) (array([0., 1.], dtype=float32), array([1348624,   56304]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1053047,   50825]))\n",
      "torch.Size([1, 3, 20, 224, 224]) torch.Size([1, 1, 20, 224, 224]) (array([0., 1.], dtype=float32), array([960120,  43400]))\n",
      "torch.Size([1, 3, 22, 224, 224]) torch.Size([1, 1, 22, 224, 224]) (array([0., 1.], dtype=float32), array([1046558,   57314]))\n",
      "torch.Size([1, 3, 21, 224, 224]) torch.Size([1, 1, 21, 224, 224]) (array([0., 1.], dtype=float32), array([1015892,   37804]))\n",
      "torch.Size([1, 3, 13, 224, 224]) torch.Size([1, 1, 13, 224, 224]) (array([0., 1.], dtype=float32), array([624962,  27326]))\n",
      "torch.Size([1, 3, 19, 224, 224]) torch.Size([1, 1, 19, 224, 224]) (array([0., 1.], dtype=float32), array([912334,  41010]))\n",
      "torch.Size([1, 3, 18, 224, 224]) torch.Size([1, 1, 18, 224, 224]) (array([0., 1.], dtype=float32), array([856627,  46541]))\n",
      "torch.Size([1, 3, 28, 224, 224]) torch.Size([1, 1, 28, 224, 224]) (array([0., 1.], dtype=float32), array([1352009,   52919]))\n",
      "torch.Size([1, 3, 24, 224, 224]) torch.Size([1, 1, 24, 224, 224]) (array([0., 1.], dtype=float32), array([1153719,   50505]))\n",
      "torch.Size([1, 3, 19, 224, 224]) torch.Size([1, 1, 19, 224, 224]) (array([0., 1.], dtype=float32), array([908265,  45079]))\n"
     ]
    }
   ],
   "source": [
    "step=0\n",
    "for batch_data in val_loader:\n",
    "    step+=1\n",
    "    inputs, labels = (\n",
    "        batch_data[\"image\"],\n",
    "        batch_data[\"label\"],\n",
    "    )\n",
    "    print(inputs.shape, labels.shape, np.unique(labels,return_counts=True))\n",
    "    # if step==5:\n",
    "    #     break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "57ab5a40-13d6-4679-9d95-b6bb06ad8c6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "device = torch.device(\"cuda:0\")\n",
    "device = torch.device(\"cpu\")\n",
    "\n",
    "num_classes = 2 \n",
    "UNet_meatdata = {\n",
    "    \"spatial_dims\": 3,\n",
    "    \"in_channels\": 3,\n",
    "    \"out_channels\": num_classes,\n",
    "    \"strides\": (2, 2, 2, ),\n",
    "    \"num_res_units\": 2,\n",
    "    \"channels\":(4, 8, 16, 32),\n",
    "    \"norm\": Norm.BATCH,\n",
    "}\n",
    "dataset_name = 'CAMUS'\n",
    "model = UNet(**UNet_meatdata).to(device)\n",
    "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "loss_type = \"DiceLoss\"\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "dice_metric = DiceMetric(include_background=False, reduction=\"mean\")\n",
    "model_name = f'{dataset_name}_{model.__class__.__name__}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "f2ebdcfd-7f4f-4421-b258-11e932182849",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/600\n",
      "1/16, train_loss: 0.4425\n",
      "2/16, train_loss: 0.4138\n",
      "3/16, train_loss: 0.4984\n",
      "4/16, train_loss: 0.4446\n",
      "5/16, train_loss: 0.4523\n",
      "6/16, train_loss: 0.4641\n",
      "7/16, train_loss: 0.4805\n",
      "8/16, train_loss: 0.4415\n",
      "9/16, train_loss: 0.4922\n",
      "10/16, train_loss: 0.4943\n",
      "11/16, train_loss: 0.5353\n",
      "12/16, train_loss: 0.4329\n",
      "13/16, train_loss: 0.4101\n",
      "14/16, train_loss: 0.5409\n",
      "15/16, train_loss: 0.5043\n",
      "16/16, train_loss: 0.4857\n",
      "17/16, train_loss: 0.5049\n",
      "epoch 1 average loss: 0.4728\n",
      "----------\n",
      "epoch 2/600\n",
      "1/16, train_loss: 0.4494\n",
      "2/16, train_loss: 0.4685\n",
      "3/16, train_loss: 0.5345\n",
      "4/16, train_loss: 0.5184\n",
      "5/16, train_loss: 0.4087\n",
      "6/16, train_loss: 0.4025\n",
      "7/16, train_loss: 0.5026\n",
      "8/16, train_loss: 0.4013\n",
      "9/16, train_loss: 0.4577\n",
      "10/16, train_loss: 0.4774\n",
      "11/16, train_loss: 0.4170\n",
      "12/16, train_loss: 0.4182\n",
      "13/16, train_loss: 0.4308\n",
      "14/16, train_loss: 0.5245\n",
      "15/16, train_loss: 0.5034\n",
      "16/16, train_loss: 0.4501\n",
      "17/16, train_loss: 0.5706\n",
      "epoch 2 average loss: 0.4668\n",
      "----------\n",
      "epoch 3/600\n",
      "1/16, train_loss: 0.4797\n",
      "2/16, train_loss: 0.4716\n",
      "3/16, train_loss: 0.4774\n",
      "4/16, train_loss: 0.4640\n",
      "5/16, train_loss: 0.4250\n",
      "6/16, train_loss: 0.4653\n",
      "7/16, train_loss: 0.4034\n",
      "8/16, train_loss: 0.4187\n",
      "9/16, train_loss: 0.4421\n",
      "10/16, train_loss: 0.4316\n",
      "11/16, train_loss: 0.4686\n",
      "12/16, train_loss: 0.4061\n",
      "13/16, train_loss: 0.4526\n",
      "14/16, train_loss: 0.4821\n",
      "15/16, train_loss: 0.5352\n",
      "16/16, train_loss: 0.4546\n",
      "17/16, train_loss: 0.4909\n",
      "epoch 3 average loss: 0.4570\n",
      "----------\n",
      "epoch 4/600\n",
      "1/16, train_loss: 0.4203\n",
      "2/16, train_loss: 0.4341\n",
      "3/16, train_loss: 0.4724\n",
      "4/16, train_loss: 0.3953\n",
      "5/16, train_loss: 0.4599\n",
      "6/16, train_loss: 0.4787\n",
      "7/16, train_loss: 0.4693\n",
      "8/16, train_loss: 0.3669\n",
      "9/16, train_loss: 0.4557\n",
      "10/16, train_loss: 0.4252\n",
      "11/16, train_loss: 0.4547\n",
      "12/16, train_loss: 0.4726\n",
      "13/16, train_loss: 0.4788\n",
      "14/16, train_loss: 0.4888\n",
      "15/16, train_loss: 0.4675\n",
      "16/16, train_loss: 0.4239\n",
      "17/16, train_loss: 0.5112\n",
      "epoch 4 average loss: 0.4515\n",
      "----------\n",
      "epoch 5/600\n",
      "1/16, train_loss: 0.4206\n",
      "2/16, train_loss: 0.3864\n",
      "3/16, train_loss: 0.5227\n",
      "4/16, train_loss: 0.4398\n",
      "5/16, train_loss: 0.3798\n",
      "6/16, train_loss: 0.3779\n",
      "7/16, train_loss: 0.4761\n",
      "8/16, train_loss: 0.4040\n",
      "9/16, train_loss: 0.4340\n",
      "10/16, train_loss: 0.4916\n",
      "11/16, train_loss: 0.4754\n",
      "12/16, train_loss: 0.3734\n",
      "13/16, train_loss: 0.4494\n",
      "14/16, train_loss: 0.4586\n",
      "15/16, train_loss: 0.4892\n",
      "16/16, train_loss: 0.4014\n",
      "17/16, train_loss: 0.5301\n",
      "epoch 5 average loss: 0.4418\n",
      "saved new best metric model at the 5th epoch\n",
      "current epoch: 5 current mean dice: 0.4505 \n",
      "best mean dice: 0.4505  at epoch: 5\n",
      "----------\n",
      "epoch 6/600\n",
      "1/16, train_loss: 0.4481\n",
      "2/16, train_loss: 0.3553\n",
      "3/16, train_loss: 0.4456\n",
      "4/16, train_loss: 0.4550\n",
      "5/16, train_loss: 0.4285\n",
      "6/16, train_loss: 0.3775\n",
      "7/16, train_loss: 0.4472\n",
      "8/16, train_loss: 0.4184\n",
      "9/16, train_loss: 0.4728\n",
      "10/16, train_loss: 0.4860\n",
      "11/16, train_loss: 0.4589\n",
      "12/16, train_loss: 0.3827\n",
      "13/16, train_loss: 0.4203\n",
      "14/16, train_loss: 0.4524\n",
      "15/16, train_loss: 0.4500\n",
      "16/16, train_loss: 0.4392\n",
      "17/16, train_loss: 0.5080\n",
      "epoch 6 average loss: 0.4380\n",
      "----------\n",
      "epoch 7/600\n",
      "1/16, train_loss: 0.4463\n",
      "2/16, train_loss: 0.3774\n",
      "3/16, train_loss: 0.5230\n",
      "4/16, train_loss: 0.4515\n",
      "5/16, train_loss: 0.4444\n",
      "6/16, train_loss: 0.4083\n",
      "7/16, train_loss: 0.4585\n",
      "8/16, train_loss: 0.3807\n",
      "9/16, train_loss: 0.4622\n",
      "10/16, train_loss: 0.4034\n",
      "11/16, train_loss: 0.4109\n",
      "12/16, train_loss: 0.3674\n",
      "13/16, train_loss: 0.4701\n",
      "14/16, train_loss: 0.4223\n",
      "15/16, train_loss: 0.4360\n",
      "16/16, train_loss: 0.3692\n",
      "17/16, train_loss: 0.4553\n",
      "epoch 7 average loss: 0.4286\n",
      "----------\n",
      "epoch 8/600\n",
      "1/16, train_loss: 0.3680\n",
      "2/16, train_loss: 0.4392\n",
      "3/16, train_loss: 0.5085\n",
      "4/16, train_loss: 0.4420\n",
      "5/16, train_loss: 0.4685\n",
      "6/16, train_loss: 0.4258\n",
      "7/16, train_loss: 0.4785\n",
      "8/16, train_loss: 0.4409\n",
      "9/16, train_loss: 0.3935\n",
      "10/16, train_loss: 0.4535\n",
      "11/16, train_loss: 0.4556\n",
      "12/16, train_loss: 0.3899\n",
      "13/16, train_loss: 0.4288\n",
      "14/16, train_loss: 0.5134\n",
      "15/16, train_loss: 0.5381\n",
      "16/16, train_loss: 0.4514\n",
      "17/16, train_loss: 0.4366\n",
      "epoch 8 average loss: 0.4490\n",
      "----------\n",
      "epoch 9/600\n",
      "1/16, train_loss: 0.4092\n",
      "2/16, train_loss: 0.3674\n",
      "3/16, train_loss: 0.4938\n",
      "4/16, train_loss: 0.3537\n",
      "5/16, train_loss: 0.3928\n",
      "6/16, train_loss: 0.3401\n",
      "7/16, train_loss: 0.3735\n",
      "8/16, train_loss: 0.3802\n",
      "9/16, train_loss: 0.3882\n",
      "10/16, train_loss: 0.4874\n",
      "11/16, train_loss: 0.4380\n",
      "12/16, train_loss: 0.3703\n",
      "13/16, train_loss: 0.4174\n",
      "14/16, train_loss: 0.4290\n",
      "15/16, train_loss: 0.4730\n",
      "16/16, train_loss: 0.3745\n",
      "17/16, train_loss: 0.4625\n",
      "epoch 9 average loss: 0.4089\n",
      "----------\n",
      "epoch 10/600\n",
      "1/16, train_loss: 0.4402\n",
      "2/16, train_loss: 0.3922\n",
      "3/16, train_loss: 0.4944\n",
      "4/16, train_loss: 0.4710\n",
      "5/16, train_loss: 0.4450\n",
      "6/16, train_loss: 0.3426\n",
      "7/16, train_loss: 0.4222\n",
      "8/16, train_loss: 0.3572\n",
      "9/16, train_loss: 0.4150\n",
      "10/16, train_loss: 0.4111\n",
      "11/16, train_loss: 0.4195\n",
      "12/16, train_loss: 0.4060\n",
      "13/16, train_loss: 0.3512\n",
      "14/16, train_loss: 0.4334\n",
      "15/16, train_loss: 0.4742\n",
      "16/16, train_loss: 0.3981\n",
      "17/16, train_loss: 0.3658\n",
      "epoch 10 average loss: 0.4141\n",
      "saved new best metric model at the 10th epoch\n",
      "current epoch: 10 current mean dice: 0.4778 \n",
      "best mean dice: 0.4778  at epoch: 10\n",
      "----------\n",
      "epoch 11/600\n",
      "1/16, train_loss: 0.3737\n",
      "2/16, train_loss: 0.3364\n",
      "3/16, train_loss: 0.4467\n",
      "4/16, train_loss: 0.4253\n",
      "5/16, train_loss: 0.4036\n",
      "6/16, train_loss: 0.4485\n",
      "7/16, train_loss: 0.4299\n",
      "8/16, train_loss: 0.3982\n",
      "9/16, train_loss: 0.5159\n",
      "10/16, train_loss: 0.3943\n",
      "11/16, train_loss: 0.3512\n",
      "12/16, train_loss: 0.3681\n",
      "13/16, train_loss: 0.4078\n",
      "14/16, train_loss: 0.4463\n",
      "15/16, train_loss: 0.3937\n",
      "16/16, train_loss: 0.3814\n",
      "17/16, train_loss: 0.5520\n",
      "epoch 11 average loss: 0.4161\n",
      "----------\n",
      "epoch 12/600\n",
      "1/16, train_loss: 0.4808\n",
      "2/16, train_loss: 0.3797\n",
      "3/16, train_loss: 0.4610\n",
      "4/16, train_loss: 0.5165\n",
      "5/16, train_loss: 0.3732\n",
      "6/16, train_loss: 0.3582\n",
      "7/16, train_loss: 0.4673\n",
      "8/16, train_loss: 0.4183\n",
      "9/16, train_loss: 0.4295\n",
      "10/16, train_loss: 0.4153\n",
      "11/16, train_loss: 0.3566\n",
      "12/16, train_loss: 0.4163\n",
      "13/16, train_loss: 0.4236\n",
      "14/16, train_loss: 0.4593\n",
      "15/16, train_loss: 0.4368\n",
      "16/16, train_loss: 0.4151\n",
      "17/16, train_loss: 0.3773\n",
      "epoch 12 average loss: 0.4226\n",
      "----------\n",
      "epoch 13/600\n",
      "1/16, train_loss: 0.4239\n",
      "2/16, train_loss: 0.4131\n",
      "3/16, train_loss: 0.4139\n",
      "4/16, train_loss: 0.3475\n",
      "5/16, train_loss: 0.4215\n",
      "6/16, train_loss: 0.4775\n",
      "7/16, train_loss: 0.4338\n",
      "8/16, train_loss: 0.4129\n",
      "9/16, train_loss: 0.3721\n",
      "10/16, train_loss: 0.3844\n",
      "11/16, train_loss: 0.4373\n",
      "12/16, train_loss: 0.3932\n",
      "13/16, train_loss: 0.4475\n",
      "14/16, train_loss: 0.4407\n",
      "15/16, train_loss: 0.4654\n",
      "16/16, train_loss: 0.4201\n",
      "17/16, train_loss: 0.3354\n",
      "epoch 13 average loss: 0.4141\n",
      "----------\n",
      "epoch 14/600\n",
      "1/16, train_loss: 0.4053\n",
      "2/16, train_loss: 0.4159\n",
      "3/16, train_loss: 0.4996\n",
      "4/16, train_loss: 0.3592\n",
      "5/16, train_loss: 0.4124\n",
      "6/16, train_loss: 0.3488\n",
      "7/16, train_loss: 0.4894\n",
      "8/16, train_loss: 0.3861\n",
      "9/16, train_loss: 0.5262\n",
      "10/16, train_loss: 0.4605\n",
      "11/16, train_loss: 0.4285\n",
      "12/16, train_loss: 0.4211\n",
      "13/16, train_loss: 0.4408\n",
      "14/16, train_loss: 0.4108\n",
      "15/16, train_loss: 0.5002\n",
      "16/16, train_loss: 0.4614\n",
      "17/16, train_loss: 0.4453\n",
      "epoch 14 average loss: 0.4360\n",
      "----------\n",
      "epoch 15/600\n",
      "1/16, train_loss: 0.4361\n",
      "2/16, train_loss: 0.3806\n",
      "3/16, train_loss: 0.4800\n",
      "4/16, train_loss: 0.4148\n",
      "5/16, train_loss: 0.3990\n",
      "6/16, train_loss: 0.3972\n",
      "7/16, train_loss: 0.3487\n",
      "8/16, train_loss: 0.3582\n",
      "9/16, train_loss: 0.4186\n",
      "10/16, train_loss: 0.3611\n",
      "11/16, train_loss: 0.3966\n",
      "12/16, train_loss: 0.4070\n",
      "13/16, train_loss: 0.4552\n",
      "14/16, train_loss: 0.4333\n",
      "15/16, train_loss: 0.4081\n",
      "16/16, train_loss: 0.3298\n",
      "17/16, train_loss: 0.3611\n",
      "epoch 15 average loss: 0.3992\n",
      "saved new best metric model at the 15th epoch\n",
      "current epoch: 15 current mean dice: 0.4983 \n",
      "best mean dice: 0.4983  at epoch: 15\n",
      "----------\n",
      "epoch 16/600\n",
      "1/16, train_loss: 0.4426\n",
      "2/16, train_loss: 0.3285\n",
      "3/16, train_loss: 0.5357\n",
      "4/16, train_loss: 0.3625\n",
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      "6/16, train_loss: 0.4019\n",
      "7/16, train_loss: 0.5079\n",
      "8/16, train_loss: 0.4166\n",
      "9/16, train_loss: 0.4365\n",
      "10/16, train_loss: 0.4158\n",
      "11/16, train_loss: 0.3770\n",
      "12/16, train_loss: 0.4121\n",
      "13/16, train_loss: 0.3471\n",
      "14/16, train_loss: 0.3939\n",
      "15/16, train_loss: 0.3840\n",
      "16/16, train_loss: 0.3778\n",
      "17/16, train_loss: 0.3507\n",
      "epoch 16 average loss: 0.4047\n",
      "----------\n",
      "epoch 17/600\n",
      "1/16, train_loss: 0.4680\n",
      "2/16, train_loss: 0.3233\n",
      "3/16, train_loss: 0.5011\n",
      "4/16, train_loss: 0.3785\n",
      "5/16, train_loss: 0.4752\n",
      "6/16, train_loss: 0.3903\n",
      "7/16, train_loss: 0.3845\n",
      "8/16, train_loss: 0.3820\n",
      "9/16, train_loss: 0.3884\n",
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      "11/16, train_loss: 0.3948\n",
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      "14/16, train_loss: 0.4033\n",
      "15/16, train_loss: 0.4493\n",
      "16/16, train_loss: 0.4504\n",
      "17/16, train_loss: 0.3576\n",
      "epoch 17 average loss: 0.4075\n",
      "----------\n",
      "epoch 18/600\n",
      "1/16, train_loss: 0.3673\n",
      "2/16, train_loss: 0.2649\n",
      "3/16, train_loss: 0.4360\n",
      "4/16, train_loss: 0.3314\n",
      "5/16, train_loss: 0.4248\n",
      "6/16, train_loss: 0.3130\n",
      "7/16, train_loss: 0.3803\n",
      "8/16, train_loss: 0.3767\n",
      "9/16, train_loss: 0.4864\n",
      "10/16, train_loss: 0.4809\n",
      "11/16, train_loss: 0.4288\n",
      "12/16, train_loss: 0.3196\n",
      "13/16, train_loss: 0.3263\n",
      "14/16, train_loss: 0.3696\n",
      "15/16, train_loss: 0.4055\n",
      "16/16, train_loss: 0.3390\n",
      "17/16, train_loss: 0.3692\n",
      "epoch 18 average loss: 0.3776\n",
      "----------\n",
      "epoch 19/600\n",
      "1/16, train_loss: 0.3321\n",
      "2/16, train_loss: 0.3762\n",
      "3/16, train_loss: 0.3872\n",
      "4/16, train_loss: 0.4995\n",
      "5/16, train_loss: 0.3989\n",
      "6/16, train_loss: 0.3341\n",
      "7/16, train_loss: 0.3516\n",
      "8/16, train_loss: 0.2926\n",
      "9/16, train_loss: 0.3385\n",
      "10/16, train_loss: 0.3779\n",
      "11/16, train_loss: 0.4093\n",
      "12/16, train_loss: 0.3524\n",
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      "14/16, train_loss: 0.3583\n",
      "15/16, train_loss: 0.4477\n",
      "16/16, train_loss: 0.3720\n",
      "17/16, train_loss: 0.4808\n",
      "epoch 19 average loss: 0.3854\n",
      "----------\n",
      "epoch 20/600\n",
      "1/16, train_loss: 0.3469\n",
      "2/16, train_loss: 0.3274\n",
      "3/16, train_loss: 0.4603\n",
      "4/16, train_loss: 0.4567\n",
      "5/16, train_loss: 0.4030\n",
      "6/16, train_loss: 0.3518\n",
      "7/16, train_loss: 0.3498\n",
      "8/16, train_loss: 0.3874\n",
      "9/16, train_loss: 0.4873\n",
      "10/16, train_loss: 0.4172\n",
      "11/16, train_loss: 0.4240\n",
      "12/16, train_loss: 0.3432\n",
      "13/16, train_loss: 0.2876\n",
      "14/16, train_loss: 0.3368\n",
      "15/16, train_loss: 0.3790\n",
      "16/16, train_loss: 0.3263\n",
      "17/16, train_loss: 0.4759\n",
      "epoch 20 average loss: 0.3859\n",
      "saved new best metric model at the 20th epoch\n",
      "current epoch: 20 current mean dice: 0.5220 \n",
      "best mean dice: 0.5220  at epoch: 20\n",
      "----------\n",
      "epoch 21/600\n",
      "1/16, train_loss: 0.3606\n",
      "2/16, train_loss: 0.3053\n",
      "3/16, train_loss: 0.4069\n",
      "4/16, train_loss: 0.4629\n",
      "5/16, train_loss: 0.3038\n",
      "6/16, train_loss: 0.3252\n",
      "7/16, train_loss: 0.3628\n",
      "8/16, train_loss: 0.3662\n",
      "9/16, train_loss: 0.3963\n",
      "10/16, train_loss: 0.4099\n",
      "11/16, train_loss: 0.4243\n",
      "12/16, train_loss: 0.3199\n",
      "13/16, train_loss: 0.2957\n",
      "14/16, train_loss: 0.3880\n",
      "15/16, train_loss: 0.3424\n",
      "16/16, train_loss: 0.4005\n",
      "17/16, train_loss: 0.4030\n",
      "epoch 21 average loss: 0.3690\n",
      "----------\n",
      "epoch 22/600\n",
      "1/16, train_loss: 0.3970\n",
      "2/16, train_loss: 0.4142\n",
      "3/16, train_loss: 0.4239\n",
      "4/16, train_loss: 0.4129\n",
      "5/16, train_loss: 0.3324\n",
      "6/16, train_loss: 0.3186\n",
      "7/16, train_loss: 0.4090\n",
      "8/16, train_loss: 0.3531\n",
      "9/16, train_loss: 0.3573\n",
      "10/16, train_loss: 0.3564\n",
      "11/16, train_loss: 0.3781\n",
      "12/16, train_loss: 0.3155\n",
      "13/16, train_loss: 0.3783\n",
      "14/16, train_loss: 0.3798\n",
      "15/16, train_loss: 0.4967\n",
      "16/16, train_loss: 0.3915\n",
      "17/16, train_loss: 0.2969\n",
      "epoch 22 average loss: 0.3771\n",
      "----------\n",
      "epoch 23/600\n",
      "1/16, train_loss: 0.4299\n",
      "2/16, train_loss: 0.2730\n",
      "3/16, train_loss: 0.4534\n",
      "4/16, train_loss: 0.4242\n",
      "5/16, train_loss: 0.3747\n",
      "6/16, train_loss: 0.3501\n",
      "7/16, train_loss: 0.3889\n",
      "8/16, train_loss: 0.2857\n",
      "9/16, train_loss: 0.3937\n",
      "10/16, train_loss: 0.3760\n",
      "11/16, train_loss: 0.3474\n",
      "12/16, train_loss: 0.3435\n",
      "13/16, train_loss: 0.3531\n",
      "14/16, train_loss: 0.4644\n",
      "15/16, train_loss: 0.3639\n",
      "16/16, train_loss: 0.3653\n",
      "17/16, train_loss: 0.2812\n",
      "epoch 23 average loss: 0.3687\n",
      "----------\n",
      "epoch 24/600\n",
      "1/16, train_loss: 0.4773\n",
      "2/16, train_loss: 0.2916\n",
      "3/16, train_loss: 0.3938\n",
      "4/16, train_loss: 0.3564\n",
      "5/16, train_loss: 0.3124\n",
      "6/16, train_loss: 0.3575\n",
      "7/16, train_loss: 0.4129\n",
      "8/16, train_loss: 0.3421\n",
      "9/16, train_loss: 0.3623\n",
      "10/16, train_loss: 0.4201\n",
      "11/16, train_loss: 0.3810\n",
      "12/16, train_loss: 0.2950\n",
      "13/16, train_loss: 0.3815\n",
      "14/16, train_loss: 0.3830\n",
      "15/16, train_loss: 0.3884\n",
      "16/16, train_loss: 0.3577\n",
      "17/16, train_loss: 0.2823\n",
      "epoch 24 average loss: 0.3644\n",
      "----------\n",
      "epoch 25/600\n",
      "1/16, train_loss: 0.3454\n",
      "2/16, train_loss: 0.2651\n",
      "3/16, train_loss: 0.4155\n",
      "4/16, train_loss: 0.4568\n",
      "5/16, train_loss: 0.3407\n",
      "6/16, train_loss: 0.2944\n",
      "7/16, train_loss: 0.2959\n",
      "8/16, train_loss: 0.2879\n",
      "9/16, train_loss: 0.4166\n",
      "10/16, train_loss: 0.3844\n",
      "11/16, train_loss: 0.4018\n",
      "12/16, train_loss: 0.3220\n",
      "13/16, train_loss: 0.3389\n",
      "14/16, train_loss: 0.3431\n",
      "15/16, train_loss: 0.4211\n",
      "16/16, train_loss: 0.2617\n",
      "17/16, train_loss: 0.2725\n",
      "epoch 25 average loss: 0.3449\n",
      "saved new best metric model at the 25th epoch\n",
      "current epoch: 25 current mean dice: 0.5321 \n",
      "best mean dice: 0.5321  at epoch: 25\n",
      "----------\n",
      "epoch 26/600\n",
      "1/16, train_loss: 0.3809\n",
      "2/16, train_loss: 0.3043\n",
      "3/16, train_loss: 0.3969\n",
      "4/16, train_loss: 0.3024\n",
      "5/16, train_loss: 0.2847\n",
      "6/16, train_loss: 0.3381\n",
      "7/16, train_loss: 0.3718\n",
      "8/16, train_loss: 0.3030\n",
      "9/16, train_loss: 0.3952\n",
      "10/16, train_loss: 0.4728\n",
      "11/16, train_loss: 0.3947\n",
      "12/16, train_loss: 0.3292\n",
      "13/16, train_loss: 0.3281\n",
      "14/16, train_loss: 0.3390\n",
      "15/16, train_loss: 0.3907\n",
      "16/16, train_loss: 0.3322\n",
      "17/16, train_loss: 0.3343\n",
      "epoch 26 average loss: 0.3528\n",
      "----------\n",
      "epoch 27/600\n",
      "1/16, train_loss: 0.3467\n",
      "2/16, train_loss: 0.3211\n",
      "3/16, train_loss: 0.4305\n",
      "4/16, train_loss: 0.2993\n",
      "5/16, train_loss: 0.4106\n",
      "6/16, train_loss: 0.3565\n",
      "7/16, train_loss: 0.3174\n",
      "8/16, train_loss: 0.3293\n",
      "9/16, train_loss: 0.3957\n",
      "10/16, train_loss: 0.3951\n",
      "11/16, train_loss: 0.4212\n",
      "12/16, train_loss: 0.2950\n",
      "13/16, train_loss: 0.3147\n",
      "14/16, train_loss: 0.3914\n",
      "15/16, train_loss: 0.4032\n",
      "16/16, train_loss: 0.3414\n",
      "17/16, train_loss: 0.2493\n",
      "epoch 27 average loss: 0.3540\n",
      "----------\n",
      "epoch 28/600\n",
      "1/16, train_loss: 0.4421\n",
      "2/16, train_loss: 0.3129\n",
      "3/16, train_loss: 0.4062\n",
      "4/16, train_loss: 0.3775\n",
      "5/16, train_loss: 0.2882\n",
      "6/16, train_loss: 0.2744\n",
      "7/16, train_loss: 0.3561\n",
      "8/16, train_loss: 0.3305\n",
      "9/16, train_loss: 0.3916\n",
      "10/16, train_loss: 0.2999\n",
      "11/16, train_loss: 0.2988\n",
      "12/16, train_loss: 0.2883\n",
      "13/16, train_loss: 0.3726\n",
      "14/16, train_loss: 0.3400\n",
      "15/16, train_loss: 0.3638\n",
      "16/16, train_loss: 0.2955\n",
      "17/16, train_loss: 0.4707\n",
      "epoch 28 average loss: 0.3476\n",
      "----------\n",
      "epoch 29/600\n",
      "1/16, train_loss: 0.3315\n",
      "2/16, train_loss: 0.2398\n",
      "3/16, train_loss: 0.4728\n",
      "4/16, train_loss: 0.4208\n",
      "5/16, train_loss: 0.3407\n",
      "6/16, train_loss: 0.4016\n",
      "7/16, train_loss: 0.3630\n",
      "8/16, train_loss: 0.3669\n",
      "9/16, train_loss: 0.2999\n",
      "10/16, train_loss: 0.3378\n",
      "11/16, train_loss: 0.3909\n",
      "12/16, train_loss: 0.3080\n",
      "13/16, train_loss: 0.3015\n",
      "14/16, train_loss: 0.3471\n",
      "15/16, train_loss: 0.3225\n",
      "16/16, train_loss: 0.3111\n",
      "17/16, train_loss: 0.3575\n",
      "epoch 29 average loss: 0.3479\n",
      "----------\n",
      "epoch 30/600\n",
      "1/16, train_loss: 0.3012\n",
      "2/16, train_loss: 0.3029\n",
      "3/16, train_loss: 0.3682\n",
      "4/16, train_loss: 0.3021\n",
      "5/16, train_loss: 0.3273\n",
      "6/16, train_loss: 0.2454\n",
      "7/16, train_loss: 0.3431\n",
      "8/16, train_loss: 0.3079\n",
      "9/16, train_loss: 0.3474\n",
      "10/16, train_loss: 0.3284\n",
      "11/16, train_loss: 0.3904\n",
      "12/16, train_loss: 0.3393\n",
      "13/16, train_loss: 0.3023\n",
      "14/16, train_loss: 0.3082\n",
      "15/16, train_loss: 0.4214\n",
      "16/16, train_loss: 0.3419\n",
      "17/16, train_loss: 0.2974\n",
      "epoch 30 average loss: 0.3279\n",
      "saved new best metric model at the 30th epoch\n",
      "current epoch: 30 current mean dice: 0.5414 \n",
      "best mean dice: 0.5414  at epoch: 30\n",
      "----------\n",
      "epoch 31/600\n",
      "1/16, train_loss: 0.3173\n",
      "2/16, train_loss: 0.2249\n",
      "3/16, train_loss: 0.3814\n",
      "4/16, train_loss: 0.3012\n",
      "5/16, train_loss: 0.3029\n",
      "6/16, train_loss: 0.2696\n",
      "7/16, train_loss: 0.4684\n",
      "8/16, train_loss: 0.2407\n",
      "9/16, train_loss: 0.3613\n",
      "10/16, train_loss: 0.2822\n",
      "11/16, train_loss: 0.3892\n",
      "12/16, train_loss: 0.2973\n",
      "13/16, train_loss: 0.3112\n",
      "14/16, train_loss: 0.3714\n",
      "15/16, train_loss: 0.3121\n",
      "16/16, train_loss: 0.2235\n",
      "17/16, train_loss: 0.3584\n",
      "epoch 31 average loss: 0.3184\n",
      "----------\n",
      "epoch 32/600\n",
      "1/16, train_loss: 0.3109\n",
      "2/16, train_loss: 0.3003\n",
      "3/16, train_loss: 0.4802\n",
      "4/16, train_loss: 0.2969\n",
      "5/16, train_loss: 0.2862\n",
      "6/16, train_loss: 0.2777\n",
      "7/16, train_loss: 0.2978\n",
      "8/16, train_loss: 0.3403\n",
      "9/16, train_loss: 0.2949\n",
      "10/16, train_loss: 0.2657\n",
      "11/16, train_loss: 0.3836\n",
      "12/16, train_loss: 0.2719\n",
      "13/16, train_loss: 0.2978\n",
      "14/16, train_loss: 0.2931\n",
      "15/16, train_loss: 0.3816\n",
      "16/16, train_loss: 0.3182\n",
      "17/16, train_loss: 0.2304\n",
      "epoch 32 average loss: 0.3134\n",
      "----------\n",
      "epoch 33/600\n",
      "1/16, train_loss: 0.3411\n",
      "2/16, train_loss: 0.2358\n",
      "3/16, train_loss: 0.3376\n",
      "4/16, train_loss: 0.2278\n",
      "5/16, train_loss: 0.2907\n",
      "6/16, train_loss: 0.2475\n",
      "7/16, train_loss: 0.3984\n",
      "8/16, train_loss: 0.2419\n",
      "9/16, train_loss: 0.4022\n",
      "10/16, train_loss: 0.3064\n",
      "11/16, train_loss: 0.2687\n",
      "12/16, train_loss: 0.3213\n",
      "13/16, train_loss: 0.2791\n",
      "14/16, train_loss: 0.3502\n",
      "15/16, train_loss: 0.4263\n",
      "16/16, train_loss: 0.2865\n",
      "17/16, train_loss: 0.2148\n",
      "epoch 33 average loss: 0.3045\n",
      "----------\n",
      "epoch 34/600\n",
      "1/16, train_loss: 0.3315\n",
      "2/16, train_loss: 0.3354\n",
      "3/16, train_loss: 0.4277\n",
      "4/16, train_loss: 0.2279\n",
      "5/16, train_loss: 0.2610\n",
      "6/16, train_loss: 0.2780\n",
      "7/16, train_loss: 0.3172\n",
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      "9/16, train_loss: 0.2983\n",
      "10/16, train_loss: 0.4455\n",
      "11/16, train_loss: 0.2885\n",
      "12/16, train_loss: 0.2635\n",
      "13/16, train_loss: 0.2857\n",
      "14/16, train_loss: 0.2616\n",
      "15/16, train_loss: 0.3992\n",
      "16/16, train_loss: 0.2421\n",
      "17/16, train_loss: 0.2998\n",
      "epoch 34 average loss: 0.3099\n",
      "----------\n",
      "epoch 35/600\n",
      "1/16, train_loss: 0.2453\n",
      "2/16, train_loss: 0.3035\n",
      "3/16, train_loss: 0.4354\n",
      "4/16, train_loss: 0.3175\n",
      "5/16, train_loss: 0.3631\n",
      "6/16, train_loss: 0.3118\n",
      "7/16, train_loss: 0.2986\n",
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      "9/16, train_loss: 0.2442\n",
      "10/16, train_loss: 0.3672\n",
      "11/16, train_loss: 0.3878\n",
      "12/16, train_loss: 0.2878\n",
      "13/16, train_loss: 0.2601\n",
      "14/16, train_loss: 0.3306\n",
      "15/16, train_loss: 0.2968\n",
      "16/16, train_loss: 0.2241\n",
      "17/16, train_loss: 0.2549\n",
      "epoch 35 average loss: 0.3088\n",
      "saved new best metric model at the 35th epoch\n",
      "current epoch: 35 current mean dice: 0.5945 \n",
      "best mean dice: 0.5945  at epoch: 35\n",
      "----------\n",
      "epoch 36/600\n",
      "1/16, train_loss: 0.3273\n",
      "2/16, train_loss: 0.2374\n",
      "3/16, train_loss: 0.4844\n",
      "4/16, train_loss: 0.2091\n",
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      "6/16, train_loss: 0.3797\n",
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      "9/16, train_loss: 0.3761\n",
      "10/16, train_loss: 0.3520\n",
      "11/16, train_loss: 0.2491\n",
      "12/16, train_loss: 0.2408\n",
      "13/16, train_loss: 0.2829\n",
      "14/16, train_loss: 0.2650\n",
      "15/16, train_loss: 0.3292\n",
      "16/16, train_loss: 0.3588\n",
      "17/16, train_loss: 0.2266\n",
      "epoch 36 average loss: 0.2990\n",
      "----------\n",
      "epoch 37/600\n",
      "1/16, train_loss: 0.3664\n",
      "2/16, train_loss: 0.3249\n",
      "3/16, train_loss: 0.3604\n",
      "4/16, train_loss: 0.3238\n",
      "5/16, train_loss: 0.3815\n",
      "6/16, train_loss: 0.2795\n",
      "7/16, train_loss: 0.3435\n",
      "8/16, train_loss: 0.2688\n",
      "9/16, train_loss: 0.2472\n",
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      "11/16, train_loss: 0.2875\n",
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      "14/16, train_loss: 0.2527\n",
      "15/16, train_loss: 0.3953\n",
      "16/16, train_loss: 0.2609\n",
      "17/16, train_loss: 0.2664\n",
      "epoch 37 average loss: 0.3097\n",
      "----------\n",
      "epoch 38/600\n",
      "1/16, train_loss: 0.2925\n",
      "2/16, train_loss: 0.2199\n",
      "3/16, train_loss: 0.4598\n",
      "4/16, train_loss: 0.3022\n",
      "5/16, train_loss: 0.2717\n",
      "6/16, train_loss: 0.2435\n",
      "7/16, train_loss: 0.3230\n",
      "8/16, train_loss: 0.2454\n",
      "9/16, train_loss: 0.3056\n",
      "10/16, train_loss: 0.2635\n",
      "11/16, train_loss: 0.2671\n",
      "12/16, train_loss: 0.2464\n",
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      "14/16, train_loss: 0.3193\n",
      "15/16, train_loss: 0.3728\n",
      "16/16, train_loss: 0.2398\n",
      "17/16, train_loss: 0.4004\n",
      "epoch 38 average loss: 0.2965\n",
      "----------\n",
      "epoch 39/600\n",
      "1/16, train_loss: 0.4320\n",
      "2/16, train_loss: 0.3256\n",
      "3/16, train_loss: 0.2852\n",
      "4/16, train_loss: 0.2678\n",
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      "6/16, train_loss: 0.2697\n",
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      "9/16, train_loss: 0.2828\n",
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      "11/16, train_loss: 0.3294\n",
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      "14/16, train_loss: 0.2993\n",
      "15/16, train_loss: 0.2892\n",
      "16/16, train_loss: 0.1985\n",
      "17/16, train_loss: 0.3389\n",
      "epoch 39 average loss: 0.2884\n",
      "----------\n",
      "epoch 40/600\n",
      "1/16, train_loss: 0.1985\n",
      "2/16, train_loss: 0.2457\n",
      "3/16, train_loss: 0.3873\n",
      "4/16, train_loss: 0.4160\n",
      "5/16, train_loss: 0.3100\n",
      "6/16, train_loss: 0.2797\n",
      "7/16, train_loss: 0.2754\n",
      "8/16, train_loss: 0.2324\n",
      "9/16, train_loss: 0.3007\n",
      "10/16, train_loss: 0.2897\n",
      "11/16, train_loss: 0.2334\n",
      "12/16, train_loss: 0.2461\n",
      "13/16, train_loss: 0.2525\n",
      "14/16, train_loss: 0.3159\n",
      "15/16, train_loss: 0.4131\n",
      "16/16, train_loss: 0.1916\n",
      "17/16, train_loss: 0.4432\n",
      "epoch 40 average loss: 0.2960\n",
      "saved new best metric model at the 40th epoch\n",
      "current epoch: 40 current mean dice: 0.6765 \n",
      "best mean dice: 0.6765  at epoch: 40\n",
      "----------\n",
      "epoch 41/600\n",
      "1/16, train_loss: 0.2334\n",
      "2/16, train_loss: 0.3272\n",
      "3/16, train_loss: 0.2981\n",
      "4/16, train_loss: 0.2420\n",
      "5/16, train_loss: 0.2597\n",
      "6/16, train_loss: 0.2957\n",
      "7/16, train_loss: 0.2517\n",
      "8/16, train_loss: 0.2173\n",
      "9/16, train_loss: 0.3800\n",
      "10/16, train_loss: 0.2738\n",
      "11/16, train_loss: 0.2896\n",
      "12/16, train_loss: 0.2202\n",
      "13/16, train_loss: 0.2357\n",
      "14/16, train_loss: 0.2574\n",
      "15/16, train_loss: 0.3798\n",
      "16/16, train_loss: 0.3019\n",
      "17/16, train_loss: 0.2941\n",
      "epoch 41 average loss: 0.2799\n",
      "----------\n",
      "epoch 42/600\n",
      "1/16, train_loss: 0.2912\n",
      "2/16, train_loss: 0.2089\n",
      "3/16, train_loss: 0.2746\n",
      "4/16, train_loss: 0.2400\n",
      "5/16, train_loss: 0.3072\n",
      "6/16, train_loss: 0.2615\n",
      "7/16, train_loss: 0.3033\n",
      "8/16, train_loss: 0.2972\n",
      "9/16, train_loss: 0.3592\n",
      "10/16, train_loss: 0.3184\n",
      "11/16, train_loss: 0.3373\n",
      "12/16, train_loss: 0.2450\n",
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      "14/16, train_loss: 0.2451\n",
      "15/16, train_loss: 0.2494\n",
      "16/16, train_loss: 0.3532\n",
      "17/16, train_loss: 0.2315\n",
      "epoch 42 average loss: 0.2812\n",
      "----------\n",
      "epoch 43/600\n",
      "1/16, train_loss: 0.3850\n",
      "2/16, train_loss: 0.1798\n",
      "3/16, train_loss: 0.2830\n",
      "4/16, train_loss: 0.3738\n",
      "5/16, train_loss: 0.2863\n",
      "6/16, train_loss: 0.2823\n",
      "7/16, train_loss: 0.2884\n",
      "8/16, train_loss: 0.1962\n",
      "9/16, train_loss: 0.3029\n",
      "10/16, train_loss: 0.2279\n",
      "11/16, train_loss: 0.2577\n",
      "12/16, train_loss: 0.1947\n",
      "13/16, train_loss: 0.3205\n",
      "14/16, train_loss: 0.2862\n",
      "15/16, train_loss: 0.2985\n",
      "16/16, train_loss: 0.1924\n",
      "17/16, train_loss: 0.2550\n",
      "epoch 43 average loss: 0.2712\n",
      "----------\n",
      "epoch 44/600\n",
      "1/16, train_loss: 0.2585\n",
      "2/16, train_loss: 0.1852\n",
      "3/16, train_loss: 0.2554\n",
      "4/16, train_loss: 0.2786\n",
      "5/16, train_loss: 0.3065\n",
      "6/16, train_loss: 0.2659\n",
      "7/16, train_loss: 0.2947\n",
      "8/16, train_loss: 0.2232\n",
      "9/16, train_loss: 0.2021\n",
      "10/16, train_loss: 0.3254\n",
      "11/16, train_loss: 0.2642\n",
      "12/16, train_loss: 0.2534\n",
      "13/16, train_loss: 0.1957\n",
      "14/16, train_loss: 0.2349\n",
      "15/16, train_loss: 0.2771\n",
      "16/16, train_loss: 0.3100\n",
      "17/16, train_loss: 0.1664\n",
      "epoch 44 average loss: 0.2528\n",
      "----------\n",
      "epoch 45/600\n",
      "1/16, train_loss: 0.2332\n",
      "2/16, train_loss: 0.2701\n",
      "3/16, train_loss: 0.2995\n",
      "4/16, train_loss: 0.2284\n",
      "5/16, train_loss: 0.2812\n",
      "6/16, train_loss: 0.1925\n",
      "7/16, train_loss: 0.2672\n",
      "8/16, train_loss: 0.2272\n",
      "9/16, train_loss: 0.3381\n",
      "10/16, train_loss: 0.2257\n",
      "11/16, train_loss: 0.2901\n",
      "12/16, train_loss: 0.1920\n",
      "13/16, train_loss: 0.2426\n",
      "14/16, train_loss: 0.2698\n",
      "15/16, train_loss: 0.2901\n",
      "16/16, train_loss: 0.2200\n",
      "17/16, train_loss: 0.2337\n",
      "epoch 45 average loss: 0.2530\n",
      "saved new best metric model at the 45th epoch\n",
      "current epoch: 45 current mean dice: 0.7150 \n",
      "best mean dice: 0.7150  at epoch: 45\n",
      "----------\n",
      "epoch 46/600\n",
      "1/16, train_loss: 0.3873\n",
      "2/16, train_loss: 0.2711\n",
      "3/16, train_loss: 0.2510\n",
      "4/16, train_loss: 0.1772\n",
      "5/16, train_loss: 0.2582\n",
      "6/16, train_loss: 0.2215\n",
      "7/16, train_loss: 0.3399\n",
      "8/16, train_loss: 0.3229\n",
      "9/16, train_loss: 0.2065\n",
      "10/16, train_loss: 0.2837\n",
      "11/16, train_loss: 0.3545\n",
      "12/16, train_loss: 0.2937\n",
      "13/16, train_loss: 0.2624\n",
      "14/16, train_loss: 0.2071\n",
      "15/16, train_loss: 0.2341\n",
      "16/16, train_loss: 0.1979\n",
      "17/16, train_loss: 0.1575\n",
      "epoch 46 average loss: 0.2604\n",
      "----------\n",
      "epoch 47/600\n",
      "1/16, train_loss: 0.3353\n",
      "2/16, train_loss: 0.2971\n",
      "3/16, train_loss: 0.3399\n",
      "4/16, train_loss: 0.2203\n",
      "5/16, train_loss: 0.2044\n",
      "6/16, train_loss: 0.1998\n",
      "7/16, train_loss: 0.3126\n",
      "8/16, train_loss: 0.2195\n",
      "9/16, train_loss: 0.2826\n",
      "10/16, train_loss: 0.2832\n",
      "11/16, train_loss: 0.3698\n",
      "12/16, train_loss: 0.2227\n",
      "13/16, train_loss: 0.2204\n",
      "14/16, train_loss: 0.2115\n",
      "15/16, train_loss: 0.2777\n",
      "16/16, train_loss: 0.1840\n",
      "17/16, train_loss: 0.2254\n",
      "epoch 47 average loss: 0.2592\n",
      "----------\n",
      "epoch 48/600\n",
      "1/16, train_loss: 0.2868\n",
      "2/16, train_loss: 0.2514\n",
      "3/16, train_loss: 0.4233\n",
      "4/16, train_loss: 0.2001\n",
      "5/16, train_loss: 0.1974\n",
      "6/16, train_loss: 0.1858\n",
      "7/16, train_loss: 0.1933\n",
      "8/16, train_loss: 0.2581\n",
      "9/16, train_loss: 0.2710\n",
      "10/16, train_loss: 0.3812\n",
      "11/16, train_loss: 0.3064\n",
      "12/16, train_loss: 0.1849\n",
      "13/16, train_loss: 0.2410\n",
      "14/16, train_loss: 0.2877\n",
      "15/16, train_loss: 0.2343\n",
      "16/16, train_loss: 0.3316\n",
      "17/16, train_loss: 0.1907\n",
      "epoch 48 average loss: 0.2603\n",
      "----------\n",
      "epoch 49/600\n",
      "1/16, train_loss: 0.1967\n",
      "2/16, train_loss: 0.2024\n",
      "3/16, train_loss: 0.3608\n",
      "4/16, train_loss: 0.2605\n",
      "5/16, train_loss: 0.2737\n",
      "6/16, train_loss: 0.1849\n",
      "7/16, train_loss: 0.2230\n",
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      "9/16, train_loss: 0.2784\n",
      "10/16, train_loss: 0.2463\n",
      "11/16, train_loss: 0.2592\n",
      "12/16, train_loss: 0.1994\n",
      "13/16, train_loss: 0.1915\n",
      "14/16, train_loss: 0.4178\n",
      "15/16, train_loss: 0.2178\n",
      "16/16, train_loss: 0.1995\n",
      "17/16, train_loss: 0.3996\n",
      "epoch 49 average loss: 0.2519\n",
      "----------\n",
      "epoch 50/600\n",
      "1/16, train_loss: 0.3142\n",
      "2/16, train_loss: 0.1820\n",
      "3/16, train_loss: 0.3585\n",
      "4/16, train_loss: 0.2640\n",
      "5/16, train_loss: 0.2112\n",
      "6/16, train_loss: 0.2495\n",
      "7/16, train_loss: 0.2508\n",
      "8/16, train_loss: 0.2019\n",
      "9/16, train_loss: 0.2065\n",
      "10/16, train_loss: 0.2577\n",
      "11/16, train_loss: 0.2031\n",
      "12/16, train_loss: 0.1829\n",
      "13/16, train_loss: 0.2328\n",
      "14/16, train_loss: 0.2451\n",
      "15/16, train_loss: 0.2651\n",
      "16/16, train_loss: 0.2664\n",
      "17/16, train_loss: 0.1535\n",
      "epoch 50 average loss: 0.2379\n",
      "saved new best metric model at the 50th epoch\n",
      "current epoch: 50 current mean dice: 0.7300 \n",
      "best mean dice: 0.7300  at epoch: 50\n",
      "----------\n",
      "epoch 51/600\n",
      "1/16, train_loss: 0.1803\n",
      "2/16, train_loss: 0.1992\n",
      "3/16, train_loss: 0.3359\n",
      "4/16, train_loss: 0.2113\n",
      "5/16, train_loss: 0.2205\n",
      "6/16, train_loss: 0.1809\n",
      "7/16, train_loss: 0.2640\n",
      "8/16, train_loss: 0.1767\n",
      "9/16, train_loss: 0.2772\n",
      "10/16, train_loss: 0.2320\n",
      "11/16, train_loss: 0.3767\n",
      "12/16, train_loss: 0.2021\n",
      "13/16, train_loss: 0.1841\n",
      "14/16, train_loss: 0.2334\n",
      "15/16, train_loss: 0.2780\n",
      "16/16, train_loss: 0.1576\n",
      "17/16, train_loss: 0.1923\n",
      "epoch 51 average loss: 0.2295\n",
      "----------\n",
      "epoch 52/600\n",
      "1/16, train_loss: 0.2664\n",
      "2/16, train_loss: 0.1859\n",
      "3/16, train_loss: 0.3356\n",
      "4/16, train_loss: 0.2699\n",
      "5/16, train_loss: 0.2074\n",
      "6/16, train_loss: 0.2132\n",
      "7/16, train_loss: 0.2293\n",
      "8/16, train_loss: 0.2069\n",
      "9/16, train_loss: 0.2030\n",
      "10/16, train_loss: 0.2776\n",
      "11/16, train_loss: 0.2039\n",
      "12/16, train_loss: 0.1789\n",
      "13/16, train_loss: 0.1627\n",
      "14/16, train_loss: 0.1814\n",
      "15/16, train_loss: 0.2646\n",
      "16/16, train_loss: 0.2693\n",
      "17/16, train_loss: 0.1854\n",
      "epoch 52 average loss: 0.2260\n",
      "----------\n",
      "epoch 53/600\n",
      "1/16, train_loss: 0.1657\n",
      "2/16, train_loss: 0.2218\n",
      "3/16, train_loss: 0.3650\n",
      "4/16, train_loss: 0.2392\n",
      "5/16, train_loss: 0.1945\n",
      "6/16, train_loss: 0.2464\n",
      "7/16, train_loss: 0.2028\n",
      "8/16, train_loss: 0.1845\n",
      "9/16, train_loss: 0.1576\n",
      "10/16, train_loss: 0.2065\n",
      "11/16, train_loss: 0.1744\n",
      "12/16, train_loss: 0.1687\n",
      "13/16, train_loss: 0.1604\n",
      "14/16, train_loss: 0.2210\n",
      "15/16, train_loss: 0.2222\n",
      "16/16, train_loss: 0.2438\n",
      "17/16, train_loss: 0.4235\n",
      "epoch 53 average loss: 0.2234\n",
      "----------\n",
      "epoch 54/600\n",
      "1/16, train_loss: 0.2131\n",
      "2/16, train_loss: 0.1908\n",
      "3/16, train_loss: 0.3087\n",
      "4/16, train_loss: 0.2835\n",
      "5/16, train_loss: 0.2069\n",
      "6/16, train_loss: 0.1842\n",
      "7/16, train_loss: 0.2351\n",
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      "9/16, train_loss: 0.2802\n",
      "10/16, train_loss: 0.3371\n",
      "11/16, train_loss: 0.1960\n",
      "12/16, train_loss: 0.1982\n",
      "13/16, train_loss: 0.2373\n",
      "14/16, train_loss: 0.1786\n",
      "15/16, train_loss: 0.3018\n",
      "16/16, train_loss: 0.1371\n",
      "17/16, train_loss: 0.3005\n",
      "epoch 54 average loss: 0.2356\n",
      "----------\n",
      "epoch 55/600\n",
      "1/16, train_loss: 0.2534\n",
      "2/16, train_loss: 0.2509\n",
      "3/16, train_loss: 0.3096\n",
      "4/16, train_loss: 0.2926\n",
      "5/16, train_loss: 0.1789\n",
      "6/16, train_loss: 0.1636\n",
      "7/16, train_loss: 0.1946\n",
      "8/16, train_loss: 0.1692\n",
      "9/16, train_loss: 0.1736\n",
      "10/16, train_loss: 0.2831\n",
      "11/16, train_loss: 0.2840\n",
      "12/16, train_loss: 0.1983\n",
      "13/16, train_loss: 0.2426\n",
      "14/16, train_loss: 0.2475\n",
      "15/16, train_loss: 0.3496\n",
      "16/16, train_loss: 0.3165\n",
      "17/16, train_loss: 0.1328\n",
      "epoch 55 average loss: 0.2377\n",
      "saved new best metric model at the 55th epoch\n",
      "current epoch: 55 current mean dice: 0.7358 \n",
      "best mean dice: 0.7358  at epoch: 55\n",
      "----------\n",
      "epoch 56/600\n",
      "1/16, train_loss: 0.1733\n",
      "2/16, train_loss: 0.1739\n",
      "3/16, train_loss: 0.2696\n",
      "4/16, train_loss: 0.2397\n",
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      "6/16, train_loss: 0.1654\n",
      "7/16, train_loss: 0.2195\n",
      "8/16, train_loss: 0.1966\n",
      "9/16, train_loss: 0.2272\n",
      "10/16, train_loss: 0.2081\n",
      "11/16, train_loss: 0.1644\n",
      "12/16, train_loss: 0.1942\n",
      "13/16, train_loss: 0.1812\n",
      "14/16, train_loss: 0.1704\n",
      "15/16, train_loss: 0.1902\n",
      "16/16, train_loss: 0.1938\n",
      "17/16, train_loss: 0.1491\n",
      "epoch 56 average loss: 0.1959\n",
      "----------\n",
      "epoch 57/600\n",
      "1/16, train_loss: 0.1609\n",
      "2/16, train_loss: 0.1718\n",
      "3/16, train_loss: 0.2881\n",
      "4/16, train_loss: 0.1682\n",
      "5/16, train_loss: 0.1767\n",
      "6/16, train_loss: 0.1912\n",
      "7/16, train_loss: 0.1596\n",
      "8/16, train_loss: 0.1826\n",
      "9/16, train_loss: 0.2611\n",
      "10/16, train_loss: 0.1814\n",
      "11/16, train_loss: 0.1832\n",
      "12/16, train_loss: 0.1552\n",
      "13/16, train_loss: 0.1547\n",
      "14/16, train_loss: 0.2297\n",
      "15/16, train_loss: 0.2096\n",
      "16/16, train_loss: 0.2544\n",
      "17/16, train_loss: 0.1201\n",
      "epoch 57 average loss: 0.1911\n",
      "----------\n",
      "epoch 58/600\n",
      "1/16, train_loss: 0.2232\n",
      "2/16, train_loss: 0.1617\n",
      "3/16, train_loss: 0.2098\n",
      "4/16, train_loss: 0.2769\n",
      "5/16, train_loss: 0.2758\n",
      "6/16, train_loss: 0.1872\n",
      "7/16, train_loss: 0.2117\n",
      "8/16, train_loss: 0.1680\n",
      "9/16, train_loss: 0.1995\n",
      "10/16, train_loss: 0.3275\n",
      "11/16, train_loss: 0.2104\n",
      "12/16, train_loss: 0.2687\n",
      "13/16, train_loss: 0.2042\n",
      "14/16, train_loss: 0.1954\n",
      "15/16, train_loss: 0.2948\n",
      "16/16, train_loss: 0.1413\n",
      "17/16, train_loss: 0.2387\n",
      "epoch 58 average loss: 0.2232\n",
      "----------\n",
      "epoch 59/600\n",
      "1/16, train_loss: 0.2516\n",
      "2/16, train_loss: 0.2362\n",
      "3/16, train_loss: 0.2665\n",
      "4/16, train_loss: 0.1454\n",
      "5/16, train_loss: 0.1855\n",
      "6/16, train_loss: 0.1546\n",
      "7/16, train_loss: 0.3290\n",
      "8/16, train_loss: 0.2578\n",
      "9/16, train_loss: 0.2844\n",
      "10/16, train_loss: 0.1798\n",
      "11/16, train_loss: 0.2146\n",
      "12/16, train_loss: 0.1697\n",
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      "15/16, train_loss: 0.2223\n",
      "16/16, train_loss: 0.1448\n",
      "17/16, train_loss: 0.2435\n",
      "epoch 59 average loss: 0.2155\n",
      "----------\n",
      "epoch 60/600\n",
      "1/16, train_loss: 0.2332\n",
      "2/16, train_loss: 0.1558\n",
      "3/16, train_loss: 0.2355\n",
      "4/16, train_loss: 0.1555\n",
      "5/16, train_loss: 0.2122\n",
      "6/16, train_loss: 0.1402\n",
      "7/16, train_loss: 0.2880\n",
      "8/16, train_loss: 0.1838\n",
      "9/16, train_loss: 0.1720\n",
      "10/16, train_loss: 0.1821\n",
      "11/16, train_loss: 0.1882\n",
      "12/16, train_loss: 0.2203\n",
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      "15/16, train_loss: 0.2225\n",
      "16/16, train_loss: 0.2210\n",
      "17/16, train_loss: 0.2094\n",
      "epoch 60 average loss: 0.2049\n",
      "saved new best metric model at the 60th epoch\n",
      "current epoch: 60 current mean dice: 0.7607 \n",
      "best mean dice: 0.7607  at epoch: 60\n",
      "----------\n",
      "epoch 61/600\n",
      "1/16, train_loss: 0.1541\n",
      "2/16, train_loss: 0.1324\n",
      "3/16, train_loss: 0.2686\n",
      "4/16, train_loss: 0.1980\n",
      "5/16, train_loss: 0.1983\n",
      "6/16, train_loss: 0.1652\n",
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      "9/16, train_loss: 0.2600\n",
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      "11/16, train_loss: 0.2905\n",
      "12/16, train_loss: 0.1758\n",
      "13/16, train_loss: 0.1433\n",
      "14/16, train_loss: 0.1660\n",
      "15/16, train_loss: 0.1792\n",
      "16/16, train_loss: 0.1665\n",
      "17/16, train_loss: 0.2295\n",
      "epoch 61 average loss: 0.1883\n",
      "----------\n",
      "epoch 62/600\n",
      "1/16, train_loss: 0.2352\n",
      "2/16, train_loss: 0.1465\n",
      "3/16, train_loss: 0.1983\n",
      "4/16, train_loss: 0.1769\n",
      "5/16, train_loss: 0.3048\n",
      "6/16, train_loss: 0.2037\n",
      "7/16, train_loss: 0.2974\n",
      "8/16, train_loss: 0.1579\n",
      "9/16, train_loss: 0.2458\n",
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      "11/16, train_loss: 0.1773\n",
      "12/16, train_loss: 0.1747\n",
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      "15/16, train_loss: 0.2366\n",
      "16/16, train_loss: 0.1832\n",
      "17/16, train_loss: 0.1444\n",
      "epoch 62 average loss: 0.2038\n",
      "----------\n",
      "epoch 63/600\n",
      "1/16, train_loss: 0.1825\n",
      "2/16, train_loss: 0.1672\n",
      "3/16, train_loss: 0.2747\n",
      "4/16, train_loss: 0.1396\n",
      "5/16, train_loss: 0.1801\n",
      "6/16, train_loss: 0.1783\n",
      "7/16, train_loss: 0.2066\n",
      "8/16, train_loss: 0.1981\n",
      "9/16, train_loss: 0.3132\n",
      "10/16, train_loss: 0.2505\n",
      "11/16, train_loss: 0.2594\n",
      "12/16, train_loss: 0.1892\n",
      "13/16, train_loss: 0.1743\n",
      "14/16, train_loss: 0.1653\n",
      "15/16, train_loss: 0.1693\n",
      "16/16, train_loss: 0.1781\n",
      "17/16, train_loss: 0.2392\n",
      "epoch 63 average loss: 0.2039\n",
      "----------\n",
      "epoch 64/600\n",
      "1/16, train_loss: 0.1361\n",
      "2/16, train_loss: 0.1277\n",
      "3/16, train_loss: 0.1735\n",
      "4/16, train_loss: 0.2655\n",
      "5/16, train_loss: 0.1650\n",
      "6/16, train_loss: 0.1593\n",
      "7/16, train_loss: 0.2809\n",
      "8/16, train_loss: 0.1318\n",
      "9/16, train_loss: 0.1305\n",
      "10/16, train_loss: 0.1390\n",
      "11/16, train_loss: 0.1723\n",
      "12/16, train_loss: 0.1756\n",
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      "14/16, train_loss: 0.2473\n",
      "15/16, train_loss: 0.3155\n",
      "16/16, train_loss: 0.1881\n",
      "17/16, train_loss: 0.1325\n",
      "epoch 64 average loss: 0.1884\n",
      "----------\n",
      "epoch 65/600\n",
      "1/16, train_loss: 0.1912\n",
      "2/16, train_loss: 0.1243\n",
      "3/16, train_loss: 0.3207\n",
      "4/16, train_loss: 0.1317\n",
      "5/16, train_loss: 0.2038\n",
      "6/16, train_loss: 0.1657\n",
      "7/16, train_loss: 0.2946\n",
      "8/16, train_loss: 0.1548\n",
      "9/16, train_loss: 0.1531\n",
      "10/16, train_loss: 0.2075\n",
      "11/16, train_loss: 0.1406\n",
      "12/16, train_loss: 0.1589\n",
      "13/16, train_loss: 0.2600\n",
      "14/16, train_loss: 0.2346\n",
      "15/16, train_loss: 0.1701\n",
      "16/16, train_loss: 0.1457\n",
      "17/16, train_loss: 0.1637\n",
      "epoch 65 average loss: 0.1895\n",
      "saved new best metric model at the 65th epoch\n",
      "current epoch: 65 current mean dice: 0.7721 \n",
      "best mean dice: 0.7721  at epoch: 65\n",
      "----------\n",
      "epoch 66/600\n",
      "1/16, train_loss: 0.1470\n",
      "2/16, train_loss: 0.2171\n",
      "3/16, train_loss: 0.3556\n",
      "4/16, train_loss: 0.2445\n",
      "5/16, train_loss: 0.1625\n",
      "6/16, train_loss: 0.1548\n",
      "7/16, train_loss: 0.2831\n",
      "8/16, train_loss: 0.1558\n",
      "9/16, train_loss: 0.2124\n",
      "10/16, train_loss: 0.1895\n",
      "11/16, train_loss: 0.3005\n",
      "12/16, train_loss: 0.1816\n",
      "13/16, train_loss: 0.1903\n",
      "14/16, train_loss: 0.1502\n",
      "15/16, train_loss: 0.1774\n",
      "16/16, train_loss: 0.1715\n",
      "17/16, train_loss: 0.1492\n",
      "epoch 66 average loss: 0.2025\n",
      "----------\n",
      "epoch 67/600\n",
      "1/16, train_loss: 0.1512\n",
      "2/16, train_loss: 0.1535\n",
      "3/16, train_loss: 0.1681\n",
      "4/16, train_loss: 0.1593\n",
      "5/16, train_loss: 0.1541\n",
      "6/16, train_loss: 0.1811\n",
      "7/16, train_loss: 0.1538\n",
      "8/16, train_loss: 0.1292\n",
      "9/16, train_loss: 0.1998\n",
      "10/16, train_loss: 0.2806\n",
      "11/16, train_loss: 0.2009\n",
      "12/16, train_loss: 0.1360\n",
      "13/16, train_loss: 0.1905\n",
      "14/16, train_loss: 0.1524\n",
      "15/16, train_loss: 0.1870\n",
      "16/16, train_loss: 0.1367\n",
      "17/16, train_loss: 0.1373\n",
      "epoch 67 average loss: 0.1689\n",
      "----------\n",
      "epoch 68/600\n",
      "1/16, train_loss: 0.1386\n",
      "2/16, train_loss: 0.1197\n",
      "3/16, train_loss: 0.2345\n",
      "4/16, train_loss: 0.1994\n",
      "5/16, train_loss: 0.1636\n",
      "6/16, train_loss: 0.1722\n",
      "7/16, train_loss: 0.2304\n",
      "8/16, train_loss: 0.1819\n",
      "9/16, train_loss: 0.3342\n",
      "10/16, train_loss: 0.2211\n",
      "11/16, train_loss: 0.1725\n",
      "12/16, train_loss: 0.1852\n",
      "13/16, train_loss: 0.1481\n",
      "14/16, train_loss: 0.2465\n",
      "15/16, train_loss: 0.3067\n",
      "16/16, train_loss: 0.1361\n",
      "17/16, train_loss: 0.1212\n",
      "epoch 68 average loss: 0.1948\n",
      "----------\n",
      "epoch 69/600\n",
      "1/16, train_loss: 0.1349\n",
      "2/16, train_loss: 0.1022\n",
      "3/16, train_loss: 0.1569\n",
      "4/16, train_loss: 0.1231\n",
      "5/16, train_loss: 0.1937\n",
      "6/16, train_loss: 0.1741\n",
      "7/16, train_loss: 0.1499\n",
      "8/16, train_loss: 0.1934\n",
      "9/16, train_loss: 0.2417\n",
      "10/16, train_loss: 0.2984\n",
      "11/16, train_loss: 0.2540\n",
      "12/16, train_loss: 0.1427\n",
      "13/16, train_loss: 0.1608\n",
      "14/16, train_loss: 0.2124\n",
      "15/16, train_loss: 0.2776\n",
      "16/16, train_loss: 0.1327\n",
      "17/16, train_loss: 0.2250\n",
      "epoch 69 average loss: 0.1867\n",
      "----------\n",
      "epoch 70/600\n",
      "1/16, train_loss: 0.1770\n",
      "2/16, train_loss: 0.2035\n",
      "3/16, train_loss: 0.4523\n",
      "4/16, train_loss: 0.1531\n",
      "5/16, train_loss: 0.1677\n",
      "6/16, train_loss: 0.1321\n",
      "7/16, train_loss: 0.1890\n",
      "8/16, train_loss: 0.1735\n",
      "9/16, train_loss: 0.1298\n",
      "10/16, train_loss: 0.2195\n",
      "11/16, train_loss: 0.2881\n",
      "12/16, train_loss: 0.1627\n",
      "13/16, train_loss: 0.1220\n",
      "14/16, train_loss: 0.2182\n",
      "15/16, train_loss: 0.2780\n",
      "16/16, train_loss: 0.1346\n",
      "17/16, train_loss: 0.1518\n",
      "epoch 70 average loss: 0.1972\n",
      "current epoch: 70 current mean dice: 0.7516 \n",
      "best mean dice: 0.7721  at epoch: 65\n",
      "----------\n",
      "epoch 71/600\n",
      "1/16, train_loss: 0.1810\n",
      "2/16, train_loss: 0.1372\n",
      "3/16, train_loss: 0.3740\n",
      "4/16, train_loss: 0.1958\n",
      "5/16, train_loss: 0.1520\n",
      "6/16, train_loss: 0.1648\n",
      "7/16, train_loss: 0.2409\n",
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      "9/16, train_loss: 0.1841\n",
      "10/16, train_loss: 0.2421\n",
      "11/16, train_loss: 0.2329\n",
      "12/16, train_loss: 0.2033\n",
      "13/16, train_loss: 0.1186\n",
      "14/16, train_loss: 0.1502\n",
      "15/16, train_loss: 0.1683\n",
      "16/16, train_loss: 0.1778\n",
      "17/16, train_loss: 0.2159\n",
      "epoch 71 average loss: 0.1940\n",
      "----------\n",
      "epoch 72/600\n",
      "1/16, train_loss: 0.1843\n",
      "2/16, train_loss: 0.1644\n",
      "3/16, train_loss: 0.1636\n",
      "4/16, train_loss: 0.2088\n",
      "5/16, train_loss: 0.2098\n",
      "6/16, train_loss: 0.1864\n",
      "7/16, train_loss: 0.1328\n",
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      "9/16, train_loss: 0.2542\n",
      "10/16, train_loss: 0.1811\n",
      "11/16, train_loss: 0.1493\n",
      "12/16, train_loss: 0.1454\n",
      "13/16, train_loss: 0.1770\n",
      "14/16, train_loss: 0.2253\n",
      "15/16, train_loss: 0.2339\n",
      "16/16, train_loss: 0.1191\n",
      "17/16, train_loss: 0.2904\n",
      "epoch 72 average loss: 0.1868\n",
      "----------\n",
      "epoch 73/600\n",
      "1/16, train_loss: 0.1811\n",
      "2/16, train_loss: 0.1200\n",
      "3/16, train_loss: 0.3290\n",
      "4/16, train_loss: 0.2251\n",
      "5/16, train_loss: 0.1600\n",
      "6/16, train_loss: 0.1794\n",
      "7/16, train_loss: 0.1681\n",
      "8/16, train_loss: 0.1989\n",
      "9/16, train_loss: 0.1560\n",
      "10/16, train_loss: 0.1419\n",
      "11/16, train_loss: 0.2380\n",
      "12/16, train_loss: 0.1564\n",
      "13/16, train_loss: 0.1937\n",
      "14/16, train_loss: 0.1455\n",
      "15/16, train_loss: 0.2248\n",
      "16/16, train_loss: 0.1379\n",
      "17/16, train_loss: 0.1174\n",
      "epoch 73 average loss: 0.1808\n",
      "----------\n",
      "epoch 74/600\n",
      "1/16, train_loss: 0.1286\n",
      "2/16, train_loss: 0.1542\n",
      "3/16, train_loss: 0.2171\n",
      "4/16, train_loss: 0.1312\n",
      "5/16, train_loss: 0.1527\n",
      "6/16, train_loss: 0.1459\n",
      "7/16, train_loss: 0.1351\n",
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      "10/16, train_loss: 0.1588\n",
      "11/16, train_loss: 0.3269\n",
      "12/16, train_loss: 0.1690\n",
      "13/16, train_loss: 0.1697\n",
      "14/16, train_loss: 0.2104\n",
      "15/16, train_loss: 0.2227\n",
      "16/16, train_loss: 0.1282\n",
      "17/16, train_loss: 0.1010\n",
      "epoch 74 average loss: 0.1658\n",
      "----------\n",
      "epoch 75/600\n",
      "1/16, train_loss: 0.1498\n",
      "2/16, train_loss: 0.1296\n",
      "3/16, train_loss: 0.3109\n",
      "4/16, train_loss: 0.1143\n",
      "5/16, train_loss: 0.1693\n",
      "6/16, train_loss: 0.1619\n",
      "7/16, train_loss: 0.1701\n",
      "8/16, train_loss: 0.1388\n",
      "9/16, train_loss: 0.1108\n",
      "10/16, train_loss: 0.1536\n",
      "11/16, train_loss: 0.2320\n",
      "12/16, train_loss: 0.1391\n",
      "13/16, train_loss: 0.2104\n",
      "14/16, train_loss: 0.1993\n",
      "15/16, train_loss: 0.1809\n",
      "16/16, train_loss: 0.1616\n",
      "17/16, train_loss: 0.1491\n",
      "epoch 75 average loss: 0.1695\n",
      "saved new best metric model at the 75th epoch\n",
      "current epoch: 75 current mean dice: 0.7769 \n",
      "best mean dice: 0.7769  at epoch: 75\n",
      "----------\n",
      "epoch 76/600\n",
      "1/16, train_loss: 0.2052\n",
      "2/16, train_loss: 0.1281\n",
      "3/16, train_loss: 0.1821\n",
      "4/16, train_loss: 0.1289\n",
      "5/16, train_loss: 0.1841\n",
      "6/16, train_loss: 0.1244\n",
      "7/16, train_loss: 0.1358\n",
      "8/16, train_loss: 0.1708\n",
      "9/16, train_loss: 0.1416\n",
      "10/16, train_loss: 0.2051\n",
      "11/16, train_loss: 0.2169\n",
      "12/16, train_loss: 0.1568\n",
      "13/16, train_loss: 0.1660\n",
      "14/16, train_loss: 0.1416\n",
      "15/16, train_loss: 0.2297\n",
      "16/16, train_loss: 0.1284\n",
      "17/16, train_loss: 0.1203\n",
      "epoch 76 average loss: 0.1627\n",
      "----------\n",
      "epoch 77/600\n",
      "1/16, train_loss: 0.1224\n",
      "2/16, train_loss: 0.1238\n",
      "3/16, train_loss: 0.2083\n",
      "4/16, train_loss: 0.1924\n",
      "5/16, train_loss: 0.1481\n",
      "6/16, train_loss: 0.1545\n",
      "7/16, train_loss: 0.2288\n",
      "8/16, train_loss: 0.1252\n",
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      "10/16, train_loss: 0.1233\n",
      "11/16, train_loss: 0.1276\n",
      "12/16, train_loss: 0.1404\n",
      "13/16, train_loss: 0.1585\n",
      "14/16, train_loss: 0.1874\n",
      "15/16, train_loss: 0.1672\n",
      "16/16, train_loss: 0.1489\n",
      "17/16, train_loss: 0.1092\n",
      "epoch 77 average loss: 0.1526\n",
      "----------\n",
      "epoch 78/600\n",
      "1/16, train_loss: 0.1205\n",
      "2/16, train_loss: 0.1270\n",
      "3/16, train_loss: 0.1775\n",
      "4/16, train_loss: 0.1577\n",
      "5/16, train_loss: 0.1612\n",
      "6/16, train_loss: 0.1412\n",
      "7/16, train_loss: 0.1719\n",
      "8/16, train_loss: 0.1194\n",
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      "10/16, train_loss: 0.1796\n",
      "11/16, train_loss: 0.1308\n",
      "12/16, train_loss: 0.2441\n",
      "13/16, train_loss: 0.1474\n",
      "14/16, train_loss: 0.1513\n",
      "15/16, train_loss: 0.1412\n",
      "16/16, train_loss: 0.1211\n",
      "17/16, train_loss: 0.2393\n",
      "epoch 78 average loss: 0.1615\n",
      "----------\n",
      "epoch 79/600\n",
      "1/16, train_loss: 0.1280\n",
      "2/16, train_loss: 0.1453\n",
      "3/16, train_loss: 0.1808\n",
      "4/16, train_loss: 0.1272\n",
      "5/16, train_loss: 0.1333\n",
      "6/16, train_loss: 0.2064\n",
      "7/16, train_loss: 0.1329\n",
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      "9/16, train_loss: 0.1156\n",
      "10/16, train_loss: 0.1571\n",
      "11/16, train_loss: 0.2039\n",
      "12/16, train_loss: 0.1392\n",
      "13/16, train_loss: 0.1245\n",
      "14/16, train_loss: 0.1805\n",
      "15/16, train_loss: 0.2726\n",
      "16/16, train_loss: 0.1743\n",
      "17/16, train_loss: 0.2159\n",
      "epoch 79 average loss: 0.1640\n",
      "----------\n",
      "epoch 80/600\n",
      "1/16, train_loss: 0.1119\n",
      "2/16, train_loss: 0.0936\n",
      "3/16, train_loss: 0.1459\n",
      "4/16, train_loss: 0.1405\n",
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      "10/16, train_loss: 0.1625\n",
      "11/16, train_loss: 0.2197\n",
      "12/16, train_loss: 0.1531\n",
      "13/16, train_loss: 0.1376\n",
      "14/16, train_loss: 0.1971\n",
      "15/16, train_loss: 0.2664\n",
      "16/16, train_loss: 0.1259\n",
      "17/16, train_loss: 0.1755\n",
      "epoch 80 average loss: 0.1574\n",
      "saved new best metric model at the 80th epoch\n",
      "current epoch: 80 current mean dice: 0.7866 \n",
      "best mean dice: 0.7866  at epoch: 80\n",
      "----------\n",
      "epoch 81/600\n",
      "1/16, train_loss: 0.1423\n",
      "2/16, train_loss: 0.1355\n",
      "3/16, train_loss: 0.1938\n",
      "4/16, train_loss: 0.2071\n",
      "5/16, train_loss: 0.1986\n",
      "6/16, train_loss: 0.1743\n",
      "7/16, train_loss: 0.2236\n",
      "8/16, train_loss: 0.1159\n",
      "9/16, train_loss: 0.1148\n",
      "10/16, train_loss: 0.1470\n",
      "11/16, train_loss: 0.1655\n",
      "12/16, train_loss: 0.1116\n",
      "13/16, train_loss: 0.1165\n",
      "14/16, train_loss: 0.1705\n",
      "15/16, train_loss: 0.2143\n",
      "16/16, train_loss: 0.1314\n",
      "17/16, train_loss: 0.1159\n",
      "epoch 81 average loss: 0.1576\n",
      "----------\n",
      "epoch 82/600\n",
      "1/16, train_loss: 0.1413\n",
      "2/16, train_loss: 0.1294\n",
      "3/16, train_loss: 0.1464\n",
      "4/16, train_loss: 0.1133\n",
      "5/16, train_loss: 0.1330\n",
      "6/16, train_loss: 0.1377\n",
      "7/16, train_loss: 0.1249\n",
      "8/16, train_loss: 0.1194\n",
      "9/16, train_loss: 0.2210\n",
      "10/16, train_loss: 0.1689\n",
      "11/16, train_loss: 0.2285\n",
      "12/16, train_loss: 0.1325\n",
      "13/16, train_loss: 0.1781\n",
      "14/16, train_loss: 0.1469\n",
      "15/16, train_loss: 0.2536\n",
      "16/16, train_loss: 0.1285\n",
      "17/16, train_loss: 0.1090\n",
      "epoch 82 average loss: 0.1537\n",
      "----------\n",
      "epoch 83/600\n",
      "1/16, train_loss: 0.1608\n",
      "2/16, train_loss: 0.1045\n",
      "3/16, train_loss: 0.2781\n",
      "4/16, train_loss: 0.1172\n",
      "5/16, train_loss: 0.1435\n",
      "6/16, train_loss: 0.1415\n",
      "7/16, train_loss: 0.1595\n",
      "8/16, train_loss: 0.1517\n",
      "9/16, train_loss: 0.1638\n",
      "10/16, train_loss: 0.1268\n",
      "11/16, train_loss: 0.1900\n",
      "12/16, train_loss: 0.1404\n",
      "13/16, train_loss: 0.1395\n",
      "14/16, train_loss: 0.2166\n",
      "15/16, train_loss: 0.2143\n",
      "16/16, train_loss: 0.1118\n",
      "17/16, train_loss: 0.2410\n",
      "epoch 83 average loss: 0.1648\n",
      "----------\n",
      "epoch 84/600\n",
      "1/16, train_loss: 0.1896\n",
      "2/16, train_loss: 0.1183\n",
      "3/16, train_loss: 0.1937\n",
      "4/16, train_loss: 0.1367\n",
      "5/16, train_loss: 0.1439\n",
      "6/16, train_loss: 0.1588\n",
      "7/16, train_loss: 0.1595\n",
      "8/16, train_loss: 0.1305\n",
      "9/16, train_loss: 0.1592\n",
      "10/16, train_loss: 0.1283\n",
      "11/16, train_loss: 0.1328\n",
      "12/16, train_loss: 0.1572\n",
      "13/16, train_loss: 0.2113\n",
      "14/16, train_loss: 0.1429\n",
      "15/16, train_loss: 0.1215\n",
      "16/16, train_loss: 0.1503\n",
      "17/16, train_loss: 0.1512\n",
      "epoch 84 average loss: 0.1521\n",
      "----------\n",
      "epoch 85/600\n",
      "1/16, train_loss: 0.1827\n",
      "2/16, train_loss: 0.1035\n",
      "3/16, train_loss: 0.1947\n",
      "4/16, train_loss: 0.1279\n",
      "5/16, train_loss: 0.1374\n",
      "6/16, train_loss: 0.1720\n",
      "7/16, train_loss: 0.1656\n",
      "8/16, train_loss: 0.1296\n",
      "9/16, train_loss: 0.1384\n",
      "10/16, train_loss: 0.1365\n",
      "11/16, train_loss: 0.1563\n",
      "12/16, train_loss: 0.1675\n",
      "13/16, train_loss: 0.1602\n",
      "14/16, train_loss: 0.2219\n",
      "15/16, train_loss: 0.1354\n",
      "16/16, train_loss: 0.1284\n",
      "17/16, train_loss: 0.1051\n",
      "epoch 85 average loss: 0.1508\n",
      "saved new best metric model at the 85th epoch\n",
      "current epoch: 85 current mean dice: 0.7947 \n",
      "best mean dice: 0.7947  at epoch: 85\n",
      "----------\n",
      "epoch 86/600\n",
      "1/16, train_loss: 0.1375\n",
      "2/16, train_loss: 0.1318\n",
      "3/16, train_loss: 0.3720\n",
      "4/16, train_loss: 0.1128\n",
      "5/16, train_loss: 0.1589\n",
      "6/16, train_loss: 0.1649\n",
      "7/16, train_loss: 0.1552\n",
      "8/16, train_loss: 0.1531\n",
      "9/16, train_loss: 0.1703\n",
      "10/16, train_loss: 0.2250\n",
      "11/16, train_loss: 0.1249\n",
      "12/16, train_loss: 0.1456\n",
      "13/16, train_loss: 0.1591\n",
      "14/16, train_loss: 0.1483\n",
      "15/16, train_loss: 0.2129\n",
      "16/16, train_loss: 0.1340\n",
      "17/16, train_loss: 0.1410\n",
      "epoch 86 average loss: 0.1675\n",
      "----------\n",
      "epoch 87/600\n",
      "1/16, train_loss: 0.1571\n",
      "2/16, train_loss: 0.1467\n",
      "3/16, train_loss: 0.1729\n",
      "4/16, train_loss: 0.1064\n",
      "5/16, train_loss: 0.1670\n",
      "6/16, train_loss: 0.1390\n",
      "7/16, train_loss: 0.1569\n",
      "8/16, train_loss: 0.1401\n",
      "9/16, train_loss: 0.1164\n",
      "10/16, train_loss: 0.1689\n",
      "11/16, train_loss: 0.1863\n",
      "12/16, train_loss: 0.1298\n",
      "13/16, train_loss: 0.1502\n",
      "14/16, train_loss: 0.1619\n",
      "15/16, train_loss: 0.1964\n",
      "16/16, train_loss: 0.1461\n",
      "17/16, train_loss: 0.1166\n",
      "epoch 87 average loss: 0.1505\n",
      "----------\n",
      "epoch 88/600\n",
      "1/16, train_loss: 0.1159\n",
      "2/16, train_loss: 0.1091\n",
      "3/16, train_loss: 0.1793\n",
      "4/16, train_loss: 0.1299\n",
      "5/16, train_loss: 0.1449\n",
      "6/16, train_loss: 0.1473\n",
      "7/16, train_loss: 0.1296\n",
      "8/16, train_loss: 0.1650\n",
      "9/16, train_loss: 0.1305\n",
      "10/16, train_loss: 0.1347\n",
      "11/16, train_loss: 0.2004\n",
      "12/16, train_loss: 0.2045\n",
      "13/16, train_loss: 0.1453\n",
      "14/16, train_loss: 0.1375\n",
      "15/16, train_loss: 0.1257\n",
      "16/16, train_loss: 0.1788\n",
      "17/16, train_loss: 0.2875\n",
      "epoch 88 average loss: 0.1568\n",
      "----------\n",
      "epoch 89/600\n",
      "1/16, train_loss: 0.1059\n",
      "2/16, train_loss: 0.1155\n",
      "3/16, train_loss: 0.1494\n",
      "4/16, train_loss: 0.1014\n",
      "5/16, train_loss: 0.1414\n",
      "6/16, train_loss: 0.1572\n",
      "7/16, train_loss: 0.1439\n",
      "8/16, train_loss: 0.1198\n",
      "9/16, train_loss: 0.1763\n",
      "10/16, train_loss: 0.2034\n",
      "11/16, train_loss: 0.1165\n",
      "12/16, train_loss: 0.1354\n",
      "13/16, train_loss: 0.2190\n",
      "14/16, train_loss: 0.1278\n",
      "15/16, train_loss: 0.1943\n",
      "16/16, train_loss: 0.1081\n",
      "17/16, train_loss: 0.1083\n",
      "epoch 89 average loss: 0.1426\n",
      "----------\n",
      "epoch 90/600\n",
      "1/16, train_loss: 0.1730\n",
      "2/16, train_loss: 0.0937\n",
      "3/16, train_loss: 0.1295\n",
      "4/16, train_loss: 0.2225\n",
      "5/16, train_loss: 0.1233\n",
      "6/16, train_loss: 0.1349\n",
      "7/16, train_loss: 0.1632\n",
      "8/16, train_loss: 0.1431\n",
      "9/16, train_loss: 0.1209\n",
      "10/16, train_loss: 0.1147\n",
      "11/16, train_loss: 0.1483\n",
      "12/16, train_loss: 0.1021\n",
      "13/16, train_loss: 0.1340\n",
      "14/16, train_loss: 0.1294\n",
      "15/16, train_loss: 0.1789\n",
      "16/16, train_loss: 0.1093\n",
      "17/16, train_loss: 0.1398\n",
      "epoch 90 average loss: 0.1389\n",
      "current epoch: 90 current mean dice: 0.7942 \n",
      "best mean dice: 0.7947  at epoch: 85\n",
      "----------\n",
      "epoch 91/600\n",
      "1/16, train_loss: 0.1418\n",
      "2/16, train_loss: 0.1011\n",
      "3/16, train_loss: 0.1272\n",
      "4/16, train_loss: 0.1064\n",
      "5/16, train_loss: 0.1329\n",
      "6/16, train_loss: 0.1521\n",
      "7/16, train_loss: 0.1244\n",
      "8/16, train_loss: 0.1660\n",
      "9/16, train_loss: 0.1192\n",
      "10/16, train_loss: 0.1762\n",
      "11/16, train_loss: 0.1435\n",
      "12/16, train_loss: 0.1295\n",
      "13/16, train_loss: 0.1259\n",
      "14/16, train_loss: 0.2328\n",
      "15/16, train_loss: 0.1576\n",
      "16/16, train_loss: 0.1109\n",
      "17/16, train_loss: 0.1448\n",
      "epoch 91 average loss: 0.1407\n",
      "----------\n",
      "epoch 92/600\n",
      "1/16, train_loss: 0.1264\n",
      "2/16, train_loss: 0.1697\n",
      "3/16, train_loss: 0.2807\n",
      "4/16, train_loss: 0.1336\n",
      "5/16, train_loss: 0.1128\n",
      "6/16, train_loss: 0.1710\n",
      "7/16, train_loss: 0.1528\n",
      "8/16, train_loss: 0.1480\n",
      "9/16, train_loss: 0.1325\n",
      "10/16, train_loss: 0.1541\n",
      "11/16, train_loss: 0.1482\n",
      "12/16, train_loss: 0.1439\n",
      "13/16, train_loss: 0.1339\n",
      "14/16, train_loss: 0.1527\n",
      "15/16, train_loss: 0.2220\n",
      "16/16, train_loss: 0.1054\n",
      "17/16, train_loss: 0.1219\n",
      "epoch 92 average loss: 0.1535\n",
      "----------\n",
      "epoch 93/600\n",
      "1/16, train_loss: 0.1260\n",
      "2/16, train_loss: 0.1058\n",
      "3/16, train_loss: 0.1891\n",
      "4/16, train_loss: 0.2137\n",
      "5/16, train_loss: 0.1235\n",
      "6/16, train_loss: 0.1357\n",
      "7/16, train_loss: 0.1684\n",
      "8/16, train_loss: 0.1278\n",
      "9/16, train_loss: 0.1983\n",
      "10/16, train_loss: 0.1230\n",
      "11/16, train_loss: 0.1138\n",
      "12/16, train_loss: 0.1714\n",
      "13/16, train_loss: 0.1416\n",
      "14/16, train_loss: 0.1468\n",
      "15/16, train_loss: 0.3101\n",
      "16/16, train_loss: 0.1133\n",
      "17/16, train_loss: 0.2766\n",
      "epoch 93 average loss: 0.1638\n",
      "----------\n",
      "epoch 94/600\n",
      "1/16, train_loss: 0.0932\n",
      "2/16, train_loss: 0.0918\n",
      "3/16, train_loss: 0.1687\n",
      "4/16, train_loss: 0.1121\n",
      "5/16, train_loss: 0.1349\n",
      "6/16, train_loss: 0.1282\n",
      "7/16, train_loss: 0.1617\n",
      "8/16, train_loss: 0.1243\n",
      "9/16, train_loss: 0.1559\n",
      "10/16, train_loss: 0.1999\n",
      "11/16, train_loss: 0.1151\n",
      "12/16, train_loss: 0.1265\n",
      "13/16, train_loss: 0.1344\n",
      "14/16, train_loss: 0.1027\n",
      "15/16, train_loss: 0.1255\n",
      "16/16, train_loss: 0.0873\n",
      "17/16, train_loss: 0.1941\n",
      "epoch 94 average loss: 0.1327\n",
      "----------\n",
      "epoch 95/600\n",
      "1/16, train_loss: 0.1272\n",
      "2/16, train_loss: 0.1107\n",
      "3/16, train_loss: 0.2834\n",
      "4/16, train_loss: 0.1449\n",
      "5/16, train_loss: 0.1906\n",
      "6/16, train_loss: 0.1380\n",
      "7/16, train_loss: 0.1562\n",
      "8/16, train_loss: 0.1147\n",
      "9/16, train_loss: 0.1315\n",
      "10/16, train_loss: 0.1153\n",
      "11/16, train_loss: 0.1750\n",
      "12/16, train_loss: 0.1403\n",
      "13/16, train_loss: 0.1009\n",
      "14/16, train_loss: 0.1718\n",
      "15/16, train_loss: 0.1783\n",
      "16/16, train_loss: 0.1129\n",
      "17/16, train_loss: 0.1137\n",
      "epoch 95 average loss: 0.1474\n",
      "current epoch: 95 current mean dice: 0.7737 \n",
      "best mean dice: 0.7947  at epoch: 85\n",
      "----------\n",
      "epoch 96/600\n",
      "1/16, train_loss: 0.1062\n",
      "2/16, train_loss: 0.1110\n",
      "3/16, train_loss: 0.2251\n",
      "4/16, train_loss: 0.1124\n",
      "5/16, train_loss: 0.1201\n",
      "6/16, train_loss: 0.1190\n",
      "7/16, train_loss: 0.1134\n",
      "8/16, train_loss: 0.1271\n",
      "9/16, train_loss: 0.1280\n",
      "10/16, train_loss: 0.1856\n",
      "11/16, train_loss: 0.1256\n",
      "12/16, train_loss: 0.1257\n",
      "13/16, train_loss: 0.1119\n",
      "14/16, train_loss: 0.1198\n",
      "15/16, train_loss: 0.1488\n",
      "16/16, train_loss: 0.1088\n",
      "17/16, train_loss: 0.1067\n",
      "epoch 96 average loss: 0.1291\n",
      "----------\n",
      "epoch 97/600\n",
      "1/16, train_loss: 0.1272\n",
      "2/16, train_loss: 0.1104\n",
      "3/16, train_loss: 0.2569\n",
      "4/16, train_loss: 0.1383\n",
      "5/16, train_loss: 0.1283\n",
      "6/16, train_loss: 0.1395\n",
      "7/16, train_loss: 0.1132\n",
      "8/16, train_loss: 0.1307\n",
      "9/16, train_loss: 0.1063\n",
      "10/16, train_loss: 0.1415\n",
      "11/16, train_loss: 0.1341\n",
      "12/16, train_loss: 0.1064\n",
      "13/16, train_loss: 0.1063\n",
      "14/16, train_loss: 0.1429\n",
      "15/16, train_loss: 0.1342\n",
      "16/16, train_loss: 0.1143\n",
      "17/16, train_loss: 0.1469\n",
      "epoch 97 average loss: 0.1340\n",
      "----------\n",
      "epoch 98/600\n",
      "1/16, train_loss: 0.1106\n",
      "2/16, train_loss: 0.0814\n",
      "3/16, train_loss: 0.1503\n",
      "4/16, train_loss: 0.0909\n",
      "5/16, train_loss: 0.1056\n",
      "6/16, train_loss: 0.1540\n",
      "7/16, train_loss: 0.2252\n",
      "8/16, train_loss: 0.1100\n",
      "9/16, train_loss: 0.1343\n",
      "10/16, train_loss: 0.1183\n",
      "11/16, train_loss: 0.1170\n",
      "12/16, train_loss: 0.1293\n",
      "13/16, train_loss: 0.1992\n",
      "14/16, train_loss: 0.1646\n",
      "15/16, train_loss: 0.1784\n",
      "16/16, train_loss: 0.0963\n",
      "17/16, train_loss: 0.0981\n",
      "epoch 98 average loss: 0.1331\n",
      "----------\n",
      "epoch 99/600\n",
      "1/16, train_loss: 0.1384\n",
      "2/16, train_loss: 0.1033\n",
      "3/16, train_loss: 0.1575\n",
      "4/16, train_loss: 0.1221\n",
      "5/16, train_loss: 0.1284\n",
      "6/16, train_loss: 0.1592\n",
      "7/16, train_loss: 0.1112\n",
      "8/16, train_loss: 0.1231\n",
      "9/16, train_loss: 0.1053\n",
      "10/16, train_loss: 0.1722\n",
      "11/16, train_loss: 0.1687\n",
      "12/16, train_loss: 0.1384\n",
      "13/16, train_loss: 0.1153\n",
      "14/16, train_loss: 0.1108\n",
      "15/16, train_loss: 0.1255\n",
      "16/16, train_loss: 0.1229\n",
      "17/16, train_loss: 0.1272\n",
      "epoch 99 average loss: 0.1311\n",
      "----------\n",
      "epoch 100/600\n",
      "1/16, train_loss: 0.0997\n",
      "2/16, train_loss: 0.1162\n",
      "3/16, train_loss: 0.1521\n",
      "4/16, train_loss: 0.1355\n",
      "5/16, train_loss: 0.1764\n",
      "6/16, train_loss: 0.1156\n",
      "7/16, train_loss: 0.1178\n",
      "8/16, train_loss: 0.1169\n",
      "9/16, train_loss: 0.1560\n",
      "10/16, train_loss: 0.1796\n",
      "11/16, train_loss: 0.1647\n",
      "12/16, train_loss: 0.1204\n",
      "13/16, train_loss: 0.1284\n",
      "14/16, train_loss: 0.1264\n",
      "15/16, train_loss: 0.2525\n",
      "16/16, train_loss: 0.1213\n",
      "17/16, train_loss: 0.1167\n",
      "epoch 100 average loss: 0.1410\n",
      "saved new best metric model at the 100th epoch\n",
      "current epoch: 100 current mean dice: 0.7951 \n",
      "best mean dice: 0.7951  at epoch: 100\n",
      "----------\n",
      "epoch 101/600\n",
      "1/16, train_loss: 0.1155\n",
      "2/16, train_loss: 0.1514\n",
      "3/16, train_loss: 0.1532\n",
      "4/16, train_loss: 0.1487\n",
      "5/16, train_loss: 0.1743\n",
      "6/16, train_loss: 0.1473\n",
      "7/16, train_loss: 0.1446\n",
      "8/16, train_loss: 0.1223\n",
      "9/16, train_loss: 0.1080\n",
      "10/16, train_loss: 0.1095\n",
      "11/16, train_loss: 0.2732\n",
      "12/16, train_loss: 0.2179\n",
      "13/16, train_loss: 0.1950\n",
      "14/16, train_loss: 0.1210\n",
      "15/16, train_loss: 0.1083\n",
      "16/16, train_loss: 0.1144\n",
      "17/16, train_loss: 0.1335\n",
      "epoch 101 average loss: 0.1493\n",
      "----------\n",
      "epoch 102/600\n",
      "1/16, train_loss: 0.1125\n",
      "2/16, train_loss: 0.0954\n",
      "3/16, train_loss: 0.1430\n",
      "4/16, train_loss: 0.1656\n",
      "5/16, train_loss: 0.1362\n",
      "6/16, train_loss: 0.1503\n",
      "7/16, train_loss: 0.1244\n",
      "8/16, train_loss: 0.1002\n",
      "9/16, train_loss: 0.1149\n",
      "10/16, train_loss: 0.1154\n",
      "11/16, train_loss: 0.1188\n",
      "12/16, train_loss: 0.1184\n",
      "13/16, train_loss: 0.1175\n",
      "14/16, train_loss: 0.1188\n",
      "15/16, train_loss: 0.1503\n",
      "16/16, train_loss: 0.0949\n",
      "17/16, train_loss: 0.1008\n",
      "epoch 102 average loss: 0.1222\n",
      "----------\n",
      "epoch 103/600\n",
      "1/16, train_loss: 0.1151\n",
      "2/16, train_loss: 0.1083\n",
      "3/16, train_loss: 0.1940\n",
      "4/16, train_loss: 0.0901\n",
      "5/16, train_loss: 0.1298\n",
      "6/16, train_loss: 0.1156\n",
      "7/16, train_loss: 0.1260\n",
      "8/16, train_loss: 0.1344\n",
      "9/16, train_loss: 0.1847\n",
      "10/16, train_loss: 0.1346\n",
      "11/16, train_loss: 0.2182\n",
      "12/16, train_loss: 0.1375\n",
      "13/16, train_loss: 0.1089\n",
      "14/16, train_loss: 0.1180\n",
      "15/16, train_loss: 0.1683\n",
      "16/16, train_loss: 0.1239\n",
      "17/16, train_loss: 0.1149\n",
      "epoch 103 average loss: 0.1366\n",
      "----------\n",
      "epoch 104/600\n",
      "1/16, train_loss: 0.1823\n",
      "2/16, train_loss: 0.0874\n",
      "3/16, train_loss: 0.1776\n",
      "4/16, train_loss: 0.1175\n",
      "5/16, train_loss: 0.1270\n",
      "6/16, train_loss: 0.1121\n",
      "7/16, train_loss: 0.2484\n",
      "8/16, train_loss: 0.1445\n",
      "9/16, train_loss: 0.0920\n",
      "10/16, train_loss: 0.1573\n",
      "11/16, train_loss: 0.1019\n",
      "12/16, train_loss: 0.1382\n",
      "13/16, train_loss: 0.1035\n",
      "14/16, train_loss: 0.1256\n",
      "15/16, train_loss: 0.1624\n",
      "16/16, train_loss: 0.1323\n",
      "17/16, train_loss: 0.1634\n",
      "epoch 104 average loss: 0.1396\n",
      "----------\n",
      "epoch 105/600\n",
      "1/16, train_loss: 0.1051\n",
      "2/16, train_loss: 0.0963\n",
      "3/16, train_loss: 0.1941\n",
      "4/16, train_loss: 0.1135\n",
      "5/16, train_loss: 0.1069\n",
      "6/16, train_loss: 0.1220\n",
      "7/16, train_loss: 0.1107\n",
      "8/16, train_loss: 0.1258\n",
      "9/16, train_loss: 0.0757\n",
      "10/16, train_loss: 0.1129\n",
      "11/16, train_loss: 0.1183\n",
      "12/16, train_loss: 0.1877\n",
      "13/16, train_loss: 0.1097\n",
      "14/16, train_loss: 0.1008\n",
      "15/16, train_loss: 0.2422\n",
      "16/16, train_loss: 0.0992\n",
      "17/16, train_loss: 0.0616\n",
      "epoch 105 average loss: 0.1225\n",
      "current epoch: 105 current mean dice: 0.7943 \n",
      "best mean dice: 0.7951  at epoch: 100\n",
      "----------\n",
      "epoch 106/600\n",
      "1/16, train_loss: 0.1161\n",
      "2/16, train_loss: 0.0926\n",
      "3/16, train_loss: 0.1425\n",
      "4/16, train_loss: 0.1994\n",
      "5/16, train_loss: 0.1269\n",
      "6/16, train_loss: 0.1231\n",
      "7/16, train_loss: 0.0975\n",
      "8/16, train_loss: 0.1133\n",
      "9/16, train_loss: 0.1110\n",
      "10/16, train_loss: 0.1349\n",
      "11/16, train_loss: 0.1671\n",
      "12/16, train_loss: 0.1074\n",
      "13/16, train_loss: 0.1021\n",
      "14/16, train_loss: 0.1361\n",
      "15/16, train_loss: 0.1343\n",
      "16/16, train_loss: 0.1387\n",
      "17/16, train_loss: 0.0629\n",
      "epoch 106 average loss: 0.1239\n",
      "----------\n",
      "epoch 107/600\n",
      "1/16, train_loss: 0.0879\n",
      "2/16, train_loss: 0.0953\n",
      "3/16, train_loss: 0.1866\n",
      "4/16, train_loss: 0.1384\n",
      "5/16, train_loss: 0.1234\n",
      "6/16, train_loss: 0.1115\n",
      "7/16, train_loss: 0.1193\n",
      "8/16, train_loss: 0.1164\n",
      "9/16, train_loss: 0.0939\n",
      "10/16, train_loss: 0.1146\n",
      "11/16, train_loss: 0.1590\n",
      "12/16, train_loss: 0.1085\n",
      "13/16, train_loss: 0.1466\n",
      "14/16, train_loss: 0.1775\n",
      "15/16, train_loss: 0.1061\n",
      "16/16, train_loss: 0.1049\n",
      "17/16, train_loss: 0.1549\n",
      "epoch 107 average loss: 0.1262\n",
      "----------\n",
      "epoch 108/600\n",
      "1/16, train_loss: 0.1172\n",
      "2/16, train_loss: 0.1397\n",
      "3/16, train_loss: 0.1580\n",
      "4/16, train_loss: 0.1364\n",
      "5/16, train_loss: 0.1300\n",
      "6/16, train_loss: 0.1490\n",
      "7/16, train_loss: 0.1011\n",
      "8/16, train_loss: 0.1098\n",
      "9/16, train_loss: 0.0830\n",
      "10/16, train_loss: 0.1147\n",
      "11/16, train_loss: 0.1005\n",
      "12/16, train_loss: 0.1263\n",
      "13/16, train_loss: 0.1170\n",
      "14/16, train_loss: 0.1111\n",
      "15/16, train_loss: 0.1337\n",
      "16/16, train_loss: 0.1046\n",
      "17/16, train_loss: 0.0877\n",
      "epoch 108 average loss: 0.1188\n",
      "----------\n",
      "epoch 109/600\n",
      "1/16, train_loss: 0.0916\n",
      "2/16, train_loss: 0.0971\n",
      "3/16, train_loss: 0.1477\n",
      "4/16, train_loss: 0.1583\n",
      "5/16, train_loss: 0.1202\n",
      "6/16, train_loss: 0.0934\n",
      "7/16, train_loss: 0.1166\n",
      "8/16, train_loss: 0.1203\n",
      "9/16, train_loss: 0.1658\n",
      "10/16, train_loss: 0.1174\n",
      "11/16, train_loss: 0.1140\n",
      "12/16, train_loss: 0.1449\n",
      "13/16, train_loss: 0.1179\n",
      "14/16, train_loss: 0.1185\n",
      "15/16, train_loss: 0.2669\n",
      "16/16, train_loss: 0.1098\n",
      "17/16, train_loss: 0.1374\n",
      "epoch 109 average loss: 0.1316\n",
      "----------\n",
      "epoch 110/600\n",
      "1/16, train_loss: 0.1021\n",
      "2/16, train_loss: 0.1017\n",
      "3/16, train_loss: 0.1725\n",
      "4/16, train_loss: 0.0962\n",
      "5/16, train_loss: 0.1264\n",
      "6/16, train_loss: 0.1215\n",
      "7/16, train_loss: 0.1289\n",
      "8/16, train_loss: 0.1143\n",
      "9/16, train_loss: 0.1223\n",
      "10/16, train_loss: 0.1168\n",
      "11/16, train_loss: 0.1294\n",
      "12/16, train_loss: 0.1157\n",
      "13/16, train_loss: 0.1103\n",
      "14/16, train_loss: 0.1938\n",
      "15/16, train_loss: 0.1903\n",
      "16/16, train_loss: 0.0954\n",
      "17/16, train_loss: 0.1186\n",
      "epoch 110 average loss: 0.1268\n",
      "saved new best metric model at the 110th epoch\n",
      "current epoch: 110 current mean dice: 0.8017 \n",
      "best mean dice: 0.8017  at epoch: 110\n",
      "----------\n",
      "epoch 111/600\n",
      "1/16, train_loss: 0.1144\n",
      "2/16, train_loss: 0.0775\n",
      "3/16, train_loss: 0.1354\n",
      "4/16, train_loss: 0.1137\n",
      "5/16, train_loss: 0.1107\n",
      "6/16, train_loss: 0.1391\n",
      "7/16, train_loss: 0.1277\n",
      "8/16, train_loss: 0.1311\n",
      "9/16, train_loss: 0.1174\n",
      "10/16, train_loss: 0.1781\n",
      "11/16, train_loss: 0.1454\n",
      "12/16, train_loss: 0.0999\n",
      "13/16, train_loss: 0.1137\n",
      "14/16, train_loss: 0.1066\n",
      "15/16, train_loss: 0.1454\n",
      "16/16, train_loss: 0.0964\n",
      "17/16, train_loss: 0.1758\n",
      "epoch 111 average loss: 0.1252\n",
      "----------\n",
      "epoch 112/600\n",
      "1/16, train_loss: 0.1067\n",
      "2/16, train_loss: 0.1343\n",
      "3/16, train_loss: 0.1030\n",
      "4/16, train_loss: 0.1739\n",
      "5/16, train_loss: 0.1045\n",
      "6/16, train_loss: 0.1490\n",
      "7/16, train_loss: 0.1254\n",
      "8/16, train_loss: 0.1295\n",
      "9/16, train_loss: 0.1820\n",
      "10/16, train_loss: 0.1260\n",
      "11/16, train_loss: 0.1502\n",
      "12/16, train_loss: 0.0905\n",
      "13/16, train_loss: 0.1093\n",
      "14/16, train_loss: 0.1740\n",
      "15/16, train_loss: 0.1303\n",
      "16/16, train_loss: 0.1154\n",
      "17/16, train_loss: 0.0670\n",
      "epoch 112 average loss: 0.1277\n",
      "----------\n",
      "epoch 113/600\n",
      "1/16, train_loss: 0.1041\n",
      "2/16, train_loss: 0.0809\n",
      "3/16, train_loss: 0.1684\n",
      "4/16, train_loss: 0.0967\n",
      "5/16, train_loss: 0.0912\n",
      "6/16, train_loss: 0.1306\n",
      "7/16, train_loss: 0.1161\n",
      "8/16, train_loss: 0.1176\n",
      "9/16, train_loss: 0.1072\n",
      "10/16, train_loss: 0.0940\n",
      "11/16, train_loss: 0.1304\n",
      "12/16, train_loss: 0.1191\n",
      "13/16, train_loss: 0.1119\n",
      "14/16, train_loss: 0.1123\n",
      "15/16, train_loss: 0.1145\n",
      "16/16, train_loss: 0.1402\n",
      "17/16, train_loss: 0.0788\n",
      "epoch 113 average loss: 0.1126\n",
      "----------\n",
      "epoch 114/600\n",
      "1/16, train_loss: 0.1513\n",
      "2/16, train_loss: 0.0908\n",
      "3/16, train_loss: 0.1295\n",
      "4/16, train_loss: 0.1069\n",
      "5/16, train_loss: 0.1196\n",
      "6/16, train_loss: 0.1135\n",
      "7/16, train_loss: 0.0902\n",
      "8/16, train_loss: 0.0981\n",
      "9/16, train_loss: 0.1889\n",
      "10/16, train_loss: 0.1450\n",
      "11/16, train_loss: 0.1347\n",
      "12/16, train_loss: 0.1039\n",
      "13/16, train_loss: 0.0965\n",
      "14/16, train_loss: 0.1267\n",
      "15/16, train_loss: 0.1112\n",
      "16/16, train_loss: 0.1105\n",
      "17/16, train_loss: 0.0777\n",
      "epoch 114 average loss: 0.1174\n",
      "----------\n",
      "epoch 115/600\n",
      "1/16, train_loss: 0.1151\n",
      "2/16, train_loss: 0.0925\n",
      "3/16, train_loss: 0.2853\n",
      "4/16, train_loss: 0.1026\n",
      "5/16, train_loss: 0.1238\n",
      "6/16, train_loss: 0.1411\n",
      "7/16, train_loss: 0.0991\n",
      "8/16, train_loss: 0.1062\n",
      "9/16, train_loss: 0.1021\n",
      "10/16, train_loss: 0.0928\n",
      "11/16, train_loss: 0.1097\n",
      "12/16, train_loss: 0.1919\n",
      "13/16, train_loss: 0.0892\n",
      "14/16, train_loss: 0.1196\n",
      "15/16, train_loss: 0.1569\n",
      "16/16, train_loss: 0.0949\n",
      "17/16, train_loss: 0.1171\n",
      "epoch 115 average loss: 0.1259\n",
      "saved new best metric model at the 115th epoch\n",
      "current epoch: 115 current mean dice: 0.8049 \n",
      "best mean dice: 0.8049  at epoch: 115\n",
      "----------\n",
      "epoch 116/600\n",
      "1/16, train_loss: 0.1021\n",
      "2/16, train_loss: 0.1198\n",
      "3/16, train_loss: 0.1411\n",
      "4/16, train_loss: 0.1841\n",
      "5/16, train_loss: 0.1559\n",
      "6/16, train_loss: 0.1233\n",
      "7/16, train_loss: 0.1336\n",
      "8/16, train_loss: 0.1466\n",
      "9/16, train_loss: 0.0958\n",
      "10/16, train_loss: 0.1583\n",
      "11/16, train_loss: 0.1493\n",
      "12/16, train_loss: 0.1530\n",
      "13/16, train_loss: 0.1114\n",
      "14/16, train_loss: 0.1323\n",
      "15/16, train_loss: 0.1358\n",
      "16/16, train_loss: 0.1146\n",
      "17/16, train_loss: 0.0739\n",
      "epoch 116 average loss: 0.1312\n",
      "----------\n",
      "epoch 117/600\n",
      "1/16, train_loss: 0.1024\n",
      "2/16, train_loss: 0.0985\n",
      "3/16, train_loss: 0.1080\n",
      "4/16, train_loss: 0.1039\n",
      "5/16, train_loss: 0.0930\n",
      "6/16, train_loss: 0.1249\n",
      "7/16, train_loss: 0.1041\n",
      "8/16, train_loss: 0.1263\n",
      "9/16, train_loss: 0.0806\n",
      "10/16, train_loss: 0.1990\n",
      "11/16, train_loss: 0.1177\n",
      "12/16, train_loss: 0.1465\n",
      "13/16, train_loss: 0.1067\n",
      "14/16, train_loss: 0.1684\n",
      "15/16, train_loss: 0.1092\n",
      "16/16, train_loss: 0.1232\n",
      "17/16, train_loss: 0.0973\n",
      "epoch 117 average loss: 0.1182\n",
      "----------\n",
      "epoch 118/600\n",
      "1/16, train_loss: 0.1131\n",
      "2/16, train_loss: 0.0928\n",
      "3/16, train_loss: 0.0911\n",
      "4/16, train_loss: 0.1538\n",
      "5/16, train_loss: 0.1071\n",
      "6/16, train_loss: 0.1562\n",
      "7/16, train_loss: 0.1391\n",
      "8/16, train_loss: 0.1417\n",
      "9/16, train_loss: 0.1330\n",
      "10/16, train_loss: 0.1224\n",
      "11/16, train_loss: 0.1417\n",
      "12/16, train_loss: 0.1357\n",
      "13/16, train_loss: 0.1170\n",
      "14/16, train_loss: 0.1793\n",
      "15/16, train_loss: 0.1173\n",
      "16/16, train_loss: 0.0974\n",
      "17/16, train_loss: 0.0963\n",
      "epoch 118 average loss: 0.1256\n",
      "----------\n",
      "epoch 119/600\n",
      "1/16, train_loss: 0.0961\n",
      "2/16, train_loss: 0.0825\n",
      "3/16, train_loss: 0.3769\n",
      "4/16, train_loss: 0.0927\n",
      "5/16, train_loss: 0.1154\n",
      "6/16, train_loss: 0.1306\n",
      "7/16, train_loss: 0.1164\n",
      "8/16, train_loss: 0.1269\n",
      "9/16, train_loss: 0.0951\n",
      "10/16, train_loss: 0.1059\n",
      "11/16, train_loss: 0.1098\n",
      "12/16, train_loss: 0.1355\n",
      "13/16, train_loss: 0.1066\n",
      "14/16, train_loss: 0.1366\n",
      "15/16, train_loss: 0.1555\n",
      "16/16, train_loss: 0.0869\n",
      "17/16, train_loss: 0.1089\n",
      "epoch 119 average loss: 0.1281\n",
      "----------\n",
      "epoch 120/600\n",
      "1/16, train_loss: 0.1741\n",
      "2/16, train_loss: 0.0858\n",
      "3/16, train_loss: 0.1889\n",
      "4/16, train_loss: 0.0928\n",
      "5/16, train_loss: 0.0873\n",
      "6/16, train_loss: 0.1177\n",
      "7/16, train_loss: 0.1075\n",
      "8/16, train_loss: 0.1109\n",
      "9/16, train_loss: 0.0822\n",
      "10/16, train_loss: 0.1538\n",
      "11/16, train_loss: 0.0961\n",
      "12/16, train_loss: 0.1711\n",
      "13/16, train_loss: 0.0955\n",
      "14/16, train_loss: 0.1125\n",
      "15/16, train_loss: 0.2088\n",
      "16/16, train_loss: 0.1039\n",
      "17/16, train_loss: 0.1733\n",
      "epoch 120 average loss: 0.1272\n",
      "saved new best metric model at the 120th epoch\n",
      "current epoch: 120 current mean dice: 0.8139 \n",
      "best mean dice: 0.8139  at epoch: 120\n",
      "----------\n",
      "epoch 121/600\n",
      "1/16, train_loss: 0.1081\n",
      "2/16, train_loss: 0.1254\n",
      "3/16, train_loss: 0.1555\n",
      "4/16, train_loss: 0.1117\n",
      "5/16, train_loss: 0.1854\n",
      "6/16, train_loss: 0.1259\n",
      "7/16, train_loss: 0.1143\n",
      "8/16, train_loss: 0.1064\n",
      "9/16, train_loss: 0.0850\n",
      "10/16, train_loss: 0.0969\n",
      "11/16, train_loss: 0.1177\n",
      "12/16, train_loss: 0.1003\n",
      "13/16, train_loss: 0.1148\n",
      "14/16, train_loss: 0.0902\n",
      "15/16, train_loss: 0.1121\n",
      "16/16, train_loss: 0.0981\n",
      "17/16, train_loss: 0.0953\n",
      "epoch 121 average loss: 0.1143\n",
      "----------\n",
      "epoch 122/600\n",
      "1/16, train_loss: 0.1076\n",
      "2/16, train_loss: 0.0779\n",
      "3/16, train_loss: 0.2273\n",
      "4/16, train_loss: 0.0954\n",
      "5/16, train_loss: 0.1068\n",
      "6/16, train_loss: 0.0825\n",
      "7/16, train_loss: 0.1103\n",
      "8/16, train_loss: 0.1150\n",
      "9/16, train_loss: 0.1742\n",
      "10/16, train_loss: 0.1236\n",
      "11/16, train_loss: 0.0945\n",
      "12/16, train_loss: 0.1013\n",
      "13/16, train_loss: 0.0917\n",
      "14/16, train_loss: 0.1076\n",
      "15/16, train_loss: 0.1562\n",
      "16/16, train_loss: 0.1003\n",
      "17/16, train_loss: 0.0972\n",
      "epoch 122 average loss: 0.1158\n",
      "----------\n",
      "epoch 123/600\n",
      "1/16, train_loss: 0.0876\n",
      "2/16, train_loss: 0.0881\n",
      "3/16, train_loss: 0.1606\n",
      "4/16, train_loss: 0.1149\n",
      "5/16, train_loss: 0.0934\n",
      "6/16, train_loss: 0.0877\n",
      "7/16, train_loss: 0.0945\n",
      "8/16, train_loss: 0.1198\n",
      "9/16, train_loss: 0.1727\n",
      "10/16, train_loss: 0.0897\n",
      "11/16, train_loss: 0.1449\n",
      "12/16, train_loss: 0.1141\n",
      "13/16, train_loss: 0.1284\n",
      "14/16, train_loss: 0.1108\n",
      "15/16, train_loss: 0.1047\n",
      "16/16, train_loss: 0.1180\n",
      "17/16, train_loss: 0.0889\n",
      "epoch 123 average loss: 0.1129\n",
      "----------\n",
      "epoch 124/600\n",
      "1/16, train_loss: 0.1420\n",
      "2/16, train_loss: 0.0870\n",
      "3/16, train_loss: 0.1283\n",
      "4/16, train_loss: 0.0980\n",
      "5/16, train_loss: 0.1430\n",
      "6/16, train_loss: 0.1506\n",
      "7/16, train_loss: 0.1081\n",
      "8/16, train_loss: 0.1299\n",
      "9/16, train_loss: 0.0800\n",
      "10/16, train_loss: 0.0965\n",
      "11/16, train_loss: 0.1066\n",
      "12/16, train_loss: 0.1396\n",
      "13/16, train_loss: 0.0996\n",
      "14/16, train_loss: 0.1145\n",
      "15/16, train_loss: 0.1109\n",
      "16/16, train_loss: 0.1104\n",
      "17/16, train_loss: 0.1083\n",
      "epoch 124 average loss: 0.1149\n",
      "----------\n",
      "epoch 125/600\n",
      "1/16, train_loss: 0.1321\n",
      "2/16, train_loss: 0.0808\n",
      "3/16, train_loss: 0.1139\n",
      "4/16, train_loss: 0.1258\n",
      "5/16, train_loss: 0.1216\n",
      "6/16, train_loss: 0.1243\n",
      "7/16, train_loss: 0.1037\n",
      "8/16, train_loss: 0.1353\n",
      "9/16, train_loss: 0.1032\n",
      "10/16, train_loss: 0.1286\n",
      "11/16, train_loss: 0.1357\n",
      "12/16, train_loss: 0.0987\n",
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      "14/16, train_loss: 0.1189\n",
      "15/16, train_loss: 0.0978\n",
      "16/16, train_loss: 0.0924\n",
      "17/16, train_loss: 0.0879\n",
      "epoch 125 average loss: 0.1117\n",
      "current epoch: 125 current mean dice: 0.8088 \n",
      "best mean dice: 0.8139  at epoch: 120\n",
      "----------\n",
      "epoch 126/600\n",
      "1/16, train_loss: 0.1031\n",
      "2/16, train_loss: 0.0742\n",
      "3/16, train_loss: 0.2659\n",
      "4/16, train_loss: 0.1225\n",
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      "9/16, train_loss: 0.0839\n",
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      "12/16, train_loss: 0.1048\n",
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      "15/16, train_loss: 0.1422\n",
      "16/16, train_loss: 0.1029\n",
      "17/16, train_loss: 0.1270\n",
      "epoch 126 average loss: 0.1210\n",
      "----------\n",
      "epoch 127/600\n",
      "1/16, train_loss: 0.1026\n",
      "2/16, train_loss: 0.0790\n",
      "3/16, train_loss: 0.1667\n",
      "4/16, train_loss: 0.1286\n",
      "5/16, train_loss: 0.1033\n",
      "6/16, train_loss: 0.1110\n",
      "7/16, train_loss: 0.0943\n",
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      "11/16, train_loss: 0.1236\n",
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      "14/16, train_loss: 0.1219\n",
      "15/16, train_loss: 0.1799\n",
      "16/16, train_loss: 0.1257\n",
      "17/16, train_loss: 0.1030\n",
      "epoch 127 average loss: 0.1143\n",
      "----------\n",
      "epoch 128/600\n",
      "1/16, train_loss: 0.0810\n",
      "2/16, train_loss: 0.0905\n",
      "3/16, train_loss: 0.1399\n",
      "4/16, train_loss: 0.1061\n",
      "5/16, train_loss: 0.1114\n",
      "6/16, train_loss: 0.0843\n",
      "7/16, train_loss: 0.0991\n",
      "8/16, train_loss: 0.1288\n",
      "9/16, train_loss: 0.1398\n",
      "10/16, train_loss: 0.0877\n",
      "11/16, train_loss: 0.1564\n",
      "12/16, train_loss: 0.1256\n",
      "13/16, train_loss: 0.1158\n",
      "14/16, train_loss: 0.1192\n",
      "15/16, train_loss: 0.1200\n",
      "16/16, train_loss: 0.1043\n",
      "17/16, train_loss: 0.0965\n",
      "epoch 128 average loss: 0.1121\n",
      "----------\n",
      "epoch 129/600\n",
      "1/16, train_loss: 0.1190\n",
      "2/16, train_loss: 0.0843\n",
      "3/16, train_loss: 0.1199\n",
      "4/16, train_loss: 0.1148\n",
      "5/16, train_loss: 0.0983\n",
      "6/16, train_loss: 0.1110\n",
      "7/16, train_loss: 0.1081\n",
      "8/16, train_loss: 0.0902\n",
      "9/16, train_loss: 0.0920\n",
      "10/16, train_loss: 0.0970\n",
      "11/16, train_loss: 0.1240\n",
      "12/16, train_loss: 0.1207\n",
      "13/16, train_loss: 0.0944\n",
      "14/16, train_loss: 0.1228\n",
      "15/16, train_loss: 0.1325\n",
      "16/16, train_loss: 0.1150\n",
      "17/16, train_loss: 0.0833\n",
      "epoch 129 average loss: 0.1075\n",
      "----------\n",
      "epoch 130/600\n",
      "1/16, train_loss: 0.0977\n",
      "2/16, train_loss: 0.0844\n",
      "3/16, train_loss: 0.0961\n",
      "4/16, train_loss: 0.1074\n",
      "5/16, train_loss: 0.1248\n",
      "6/16, train_loss: 0.1182\n",
      "7/16, train_loss: 0.1174\n",
      "8/16, train_loss: 0.1289\n",
      "9/16, train_loss: 0.1100\n",
      "10/16, train_loss: 0.1312\n",
      "11/16, train_loss: 0.1464\n",
      "12/16, train_loss: 0.1130\n",
      "13/16, train_loss: 0.0990\n",
      "14/16, train_loss: 0.1107\n",
      "15/16, train_loss: 0.1146\n",
      "16/16, train_loss: 0.1034\n",
      "17/16, train_loss: 0.0828\n",
      "epoch 130 average loss: 0.1109\n",
      "current epoch: 130 current mean dice: 0.8116 \n",
      "best mean dice: 0.8139  at epoch: 120\n",
      "----------\n",
      "epoch 131/600\n",
      "1/16, train_loss: 0.0929\n",
      "2/16, train_loss: 0.0666\n",
      "3/16, train_loss: 0.1728\n",
      "4/16, train_loss: 0.0971\n",
      "5/16, train_loss: 0.0976\n",
      "6/16, train_loss: 0.1304\n",
      "7/16, train_loss: 0.1069\n",
      "8/16, train_loss: 0.1101\n",
      "9/16, train_loss: 0.1279\n",
      "10/16, train_loss: 0.1024\n",
      "11/16, train_loss: 0.1156\n",
      "12/16, train_loss: 0.1282\n",
      "13/16, train_loss: 0.1046\n",
      "14/16, train_loss: 0.1002\n",
      "15/16, train_loss: 0.1131\n",
      "16/16, train_loss: 0.0926\n",
      "17/16, train_loss: 0.0991\n",
      "epoch 131 average loss: 0.1093\n",
      "----------\n",
      "epoch 132/600\n",
      "1/16, train_loss: 0.0957\n",
      "2/16, train_loss: 0.0820\n",
      "3/16, train_loss: 0.2046\n",
      "4/16, train_loss: 0.1038\n",
      "5/16, train_loss: 0.1042\n",
      "6/16, train_loss: 0.1249\n",
      "7/16, train_loss: 0.0851\n",
      "8/16, train_loss: 0.1405\n",
      "9/16, train_loss: 0.1175\n",
      "10/16, train_loss: 0.1271\n",
      "11/16, train_loss: 0.1775\n",
      "12/16, train_loss: 0.1141\n",
      "13/16, train_loss: 0.1367\n",
      "14/16, train_loss: 0.1179\n",
      "15/16, train_loss: 0.1126\n",
      "16/16, train_loss: 0.1011\n",
      "17/16, train_loss: 0.0964\n",
      "epoch 132 average loss: 0.1201\n",
      "----------\n",
      "epoch 133/600\n",
      "1/16, train_loss: 0.0763\n",
      "2/16, train_loss: 0.0901\n",
      "3/16, train_loss: 0.1364\n",
      "4/16, train_loss: 0.1012\n",
      "5/16, train_loss: 0.1096\n",
      "6/16, train_loss: 0.1283\n",
      "7/16, train_loss: 0.1109\n",
      "8/16, train_loss: 0.1078\n",
      "9/16, train_loss: 0.1005\n",
      "10/16, train_loss: 0.0905\n",
      "11/16, train_loss: 0.0958\n",
      "12/16, train_loss: 0.0980\n",
      "13/16, train_loss: 0.1470\n",
      "14/16, train_loss: 0.0993\n",
      "15/16, train_loss: 0.2249\n",
      "16/16, train_loss: 0.0901\n",
      "17/16, train_loss: 0.0818\n",
      "epoch 133 average loss: 0.1111\n",
      "----------\n",
      "epoch 134/600\n",
      "1/16, train_loss: 0.1010\n",
      "2/16, train_loss: 0.0928\n",
      "3/16, train_loss: 0.2367\n",
      "4/16, train_loss: 0.1151\n",
      "5/16, train_loss: 0.1491\n",
      "6/16, train_loss: 0.1099\n",
      "7/16, train_loss: 0.0865\n",
      "8/16, train_loss: 0.1072\n",
      "9/16, train_loss: 0.1064\n",
      "10/16, train_loss: 0.1152\n",
      "11/16, train_loss: 0.2013\n",
      "12/16, train_loss: 0.1472\n",
      "13/16, train_loss: 0.0879\n",
      "14/16, train_loss: 0.1001\n",
      "15/16, train_loss: 0.0917\n",
      "16/16, train_loss: 0.1004\n",
      "17/16, train_loss: 0.1029\n",
      "epoch 134 average loss: 0.1207\n",
      "----------\n",
      "epoch 135/600\n",
      "1/16, train_loss: 0.0938\n",
      "2/16, train_loss: 0.0693\n",
      "3/16, train_loss: 0.1294\n",
      "4/16, train_loss: 0.0913\n",
      "5/16, train_loss: 0.0874\n",
      "6/16, train_loss: 0.1149\n",
      "7/16, train_loss: 0.0891\n",
      "8/16, train_loss: 0.1220\n",
      "9/16, train_loss: 0.0870\n",
      "10/16, train_loss: 0.1257\n",
      "11/16, train_loss: 0.1055\n",
      "12/16, train_loss: 0.0891\n",
      "13/16, train_loss: 0.0938\n",
      "14/16, train_loss: 0.1250\n",
      "15/16, train_loss: 0.0984\n",
      "16/16, train_loss: 0.0870\n",
      "17/16, train_loss: 0.1789\n",
      "epoch 135 average loss: 0.1051\n",
      "saved new best metric model at the 135th epoch\n",
      "current epoch: 135 current mean dice: 0.8257 \n",
      "best mean dice: 0.8257  at epoch: 135\n",
      "----------\n",
      "epoch 136/600\n",
      "1/16, train_loss: 0.0961\n",
      "2/16, train_loss: 0.0763\n",
      "3/16, train_loss: 0.2633\n",
      "4/16, train_loss: 0.1014\n",
      "5/16, train_loss: 0.0963\n",
      "6/16, train_loss: 0.0931\n",
      "7/16, train_loss: 0.1708\n",
      "8/16, train_loss: 0.1111\n",
      "9/16, train_loss: 0.0778\n",
      "10/16, train_loss: 0.1259\n",
      "11/16, train_loss: 0.2025\n",
      "12/16, train_loss: 0.1198\n",
      "13/16, train_loss: 0.0949\n",
      "14/16, train_loss: 0.1045\n",
      "15/16, train_loss: 0.1052\n",
      "16/16, train_loss: 0.0846\n",
      "17/16, train_loss: 0.0943\n",
      "epoch 136 average loss: 0.1187\n",
      "----------\n",
      "epoch 137/600\n",
      "1/16, train_loss: 0.0938\n",
      "2/16, train_loss: 0.0771\n",
      "3/16, train_loss: 0.2190\n",
      "4/16, train_loss: 0.0969\n",
      "5/16, train_loss: 0.1077\n",
      "6/16, train_loss: 0.1164\n",
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      "8/16, train_loss: 0.0919\n",
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      "10/16, train_loss: 0.0868\n",
      "11/16, train_loss: 0.1031\n",
      "12/16, train_loss: 0.1518\n",
      "13/16, train_loss: 0.0904\n",
      "14/16, train_loss: 0.2101\n",
      "15/16, train_loss: 0.1104\n",
      "16/16, train_loss: 0.0726\n",
      "17/16, train_loss: 0.0953\n",
      "epoch 137 average loss: 0.1148\n",
      "----------\n",
      "epoch 138/600\n",
      "1/16, train_loss: 0.1277\n",
      "2/16, train_loss: 0.0964\n",
      "3/16, train_loss: 0.1760\n",
      "4/16, train_loss: 0.0984\n",
      "5/16, train_loss: 0.1037\n",
      "6/16, train_loss: 0.1333\n",
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      "9/16, train_loss: 0.1149\n",
      "10/16, train_loss: 0.1033\n",
      "11/16, train_loss: 0.1315\n",
      "12/16, train_loss: 0.1064\n",
      "13/16, train_loss: 0.0906\n",
      "14/16, train_loss: 0.0915\n",
      "15/16, train_loss: 0.1046\n",
      "16/16, train_loss: 0.0951\n",
      "17/16, train_loss: 0.0814\n",
      "epoch 138 average loss: 0.1130\n",
      "----------\n",
      "epoch 139/600\n",
      "1/16, train_loss: 0.0750\n",
      "2/16, train_loss: 0.0841\n",
      "3/16, train_loss: 0.1829\n",
      "4/16, train_loss: 0.0878\n",
      "5/16, train_loss: 0.1111\n",
      "6/16, train_loss: 0.1040\n",
      "7/16, train_loss: 0.0988\n",
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      "11/16, train_loss: 0.1233\n",
      "12/16, train_loss: 0.0922\n",
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      "14/16, train_loss: 0.0868\n",
      "15/16, train_loss: 0.1276\n",
      "16/16, train_loss: 0.0782\n",
      "17/16, train_loss: 0.1762\n",
      "epoch 139 average loss: 0.1074\n",
      "----------\n",
      "epoch 140/600\n",
      "1/16, train_loss: 0.0735\n",
      "2/16, train_loss: 0.0736\n",
      "3/16, train_loss: 0.0866\n",
      "4/16, train_loss: 0.0917\n",
      "5/16, train_loss: 0.1064\n",
      "6/16, train_loss: 0.0948\n",
      "7/16, train_loss: 0.1024\n",
      "8/16, train_loss: 0.0937\n",
      "9/16, train_loss: 0.0826\n",
      "10/16, train_loss: 0.0993\n",
      "11/16, train_loss: 0.1195\n",
      "12/16, train_loss: 0.1097\n",
      "13/16, train_loss: 0.1346\n",
      "14/16, train_loss: 0.1506\n",
      "15/16, train_loss: 0.1619\n",
      "16/16, train_loss: 0.0937\n",
      "17/16, train_loss: 0.0819\n",
      "epoch 140 average loss: 0.1033\n",
      "current epoch: 140 current mean dice: 0.8194 \n",
      "best mean dice: 0.8257  at epoch: 135\n",
      "----------\n",
      "epoch 141/600\n",
      "1/16, train_loss: 0.0901\n",
      "2/16, train_loss: 0.0706\n",
      "3/16, train_loss: 0.1105\n",
      "4/16, train_loss: 0.1061\n",
      "5/16, train_loss: 0.0951\n",
      "6/16, train_loss: 0.1128\n",
      "7/16, train_loss: 0.1301\n",
      "8/16, train_loss: 0.1115\n",
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      "10/16, train_loss: 0.1199\n",
      "11/16, train_loss: 0.1167\n",
      "12/16, train_loss: 0.0882\n",
      "13/16, train_loss: 0.1068\n",
      "14/16, train_loss: 0.0909\n",
      "15/16, train_loss: 0.2292\n",
      "16/16, train_loss: 0.1096\n",
      "17/16, train_loss: 0.2008\n",
      "epoch 141 average loss: 0.1159\n",
      "----------\n",
      "epoch 142/600\n",
      "1/16, train_loss: 0.1148\n",
      "2/16, train_loss: 0.0756\n",
      "3/16, train_loss: 0.1127\n",
      "4/16, train_loss: 0.1071\n",
      "5/16, train_loss: 0.1098\n",
      "6/16, train_loss: 0.1048\n",
      "7/16, train_loss: 0.1291\n",
      "8/16, train_loss: 0.1351\n",
      "9/16, train_loss: 0.0783\n",
      "10/16, train_loss: 0.0987\n",
      "11/16, train_loss: 0.1331\n",
      "12/16, train_loss: 0.0881\n",
      "13/16, train_loss: 0.1123\n",
      "14/16, train_loss: 0.1080\n",
      "15/16, train_loss: 0.0925\n",
      "16/16, train_loss: 0.1016\n",
      "17/16, train_loss: 0.2231\n",
      "epoch 142 average loss: 0.1132\n",
      "----------\n",
      "epoch 143/600\n",
      "1/16, train_loss: 0.0848\n",
      "2/16, train_loss: 0.0794\n",
      "3/16, train_loss: 0.0918\n",
      "4/16, train_loss: 0.1582\n",
      "5/16, train_loss: 0.0938\n",
      "6/16, train_loss: 0.1243\n",
      "7/16, train_loss: 0.0901\n",
      "8/16, train_loss: 0.0891\n",
      "9/16, train_loss: 0.1156\n",
      "10/16, train_loss: 0.1075\n",
      "11/16, train_loss: 0.1427\n",
      "12/16, train_loss: 0.1167\n",
      "13/16, train_loss: 0.1036\n",
      "14/16, train_loss: 0.1158\n",
      "15/16, train_loss: 0.1099\n",
      "16/16, train_loss: 0.1016\n",
      "17/16, train_loss: 0.0739\n",
      "epoch 143 average loss: 0.1058\n",
      "----------\n",
      "epoch 144/600\n",
      "1/16, train_loss: 0.0777\n",
      "2/16, train_loss: 0.1035\n",
      "3/16, train_loss: 0.1132\n",
      "4/16, train_loss: 0.0852\n",
      "5/16, train_loss: 0.0897\n",
      "6/16, train_loss: 0.1141\n",
      "7/16, train_loss: 0.0999\n",
      "8/16, train_loss: 0.0925\n",
      "9/16, train_loss: 0.0889\n",
      "10/16, train_loss: 0.1173\n",
      "11/16, train_loss: 0.1151\n",
      "12/16, train_loss: 0.1271\n",
      "13/16, train_loss: 0.0955\n",
      "14/16, train_loss: 0.1153\n",
      "15/16, train_loss: 0.1344\n",
      "16/16, train_loss: 0.0956\n",
      "17/16, train_loss: 0.1918\n",
      "epoch 144 average loss: 0.1092\n",
      "----------\n",
      "epoch 145/600\n",
      "1/16, train_loss: 0.1362\n",
      "2/16, train_loss: 0.0923\n",
      "3/16, train_loss: 0.1112\n",
      "4/16, train_loss: 0.0826\n",
      "5/16, train_loss: 0.1124\n",
      "6/16, train_loss: 0.1224\n",
      "7/16, train_loss: 0.1122\n",
      "8/16, train_loss: 0.1093\n",
      "9/16, train_loss: 0.0864\n",
      "10/16, train_loss: 0.1019\n",
      "11/16, train_loss: 0.1166\n",
      "12/16, train_loss: 0.1495\n",
      "13/16, train_loss: 0.0828\n",
      "14/16, train_loss: 0.1048\n",
      "15/16, train_loss: 0.0956\n",
      "16/16, train_loss: 0.0977\n",
      "17/16, train_loss: 0.0837\n",
      "epoch 145 average loss: 0.1057\n",
      "saved new best metric model at the 145th epoch\n",
      "current epoch: 145 current mean dice: 0.8271 \n",
      "best mean dice: 0.8271  at epoch: 145\n",
      "----------\n",
      "epoch 146/600\n",
      "1/16, train_loss: 0.0945\n",
      "2/16, train_loss: 0.0907\n",
      "3/16, train_loss: 0.2357\n",
      "4/16, train_loss: 0.0776\n",
      "5/16, train_loss: 0.0870\n",
      "6/16, train_loss: 0.1122\n",
      "7/16, train_loss: 0.0932\n",
      "8/16, train_loss: 0.1267\n",
      "9/16, train_loss: 0.0598\n",
      "10/16, train_loss: 0.0872\n",
      "11/16, train_loss: 0.1188\n",
      "12/16, train_loss: 0.0833\n",
      "13/16, train_loss: 0.1129\n",
      "14/16, train_loss: 0.1185\n",
      "15/16, train_loss: 0.1355\n",
      "16/16, train_loss: 0.1195\n",
      "17/16, train_loss: 0.0597\n",
      "epoch 146 average loss: 0.1066\n",
      "----------\n",
      "epoch 147/600\n",
      "1/16, train_loss: 0.0851\n",
      "2/16, train_loss: 0.1169\n",
      "3/16, train_loss: 0.1274\n",
      "4/16, train_loss: 0.0904\n",
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      "6/16, train_loss: 0.1053\n",
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      "9/16, train_loss: 0.0766\n",
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      "11/16, train_loss: 0.0833\n",
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      "15/16, train_loss: 0.0857\n",
      "16/16, train_loss: 0.0960\n",
      "17/16, train_loss: 0.1197\n",
      "epoch 147 average loss: 0.0966\n",
      "----------\n",
      "epoch 148/600\n",
      "1/16, train_loss: 0.0759\n",
      "2/16, train_loss: 0.0710\n",
      "3/16, train_loss: 0.1034\n",
      "4/16, train_loss: 0.1116\n",
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      "6/16, train_loss: 0.0905\n",
      "7/16, train_loss: 0.0863\n",
      "8/16, train_loss: 0.1137\n",
      "9/16, train_loss: 0.1179\n",
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      "11/16, train_loss: 0.1581\n",
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      "15/16, train_loss: 0.0956\n",
      "16/16, train_loss: 0.0947\n",
      "17/16, train_loss: 0.1437\n",
      "epoch 148 average loss: 0.1180\n",
      "----------\n",
      "epoch 149/600\n",
      "1/16, train_loss: 0.0857\n",
      "2/16, train_loss: 0.0697\n",
      "3/16, train_loss: 0.2306\n",
      "4/16, train_loss: 0.1600\n",
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      "16/16, train_loss: 0.0800\n",
      "17/16, train_loss: 0.1216\n",
      "epoch 149 average loss: 0.1128\n",
      "----------\n",
      "epoch 150/600\n",
      "1/16, train_loss: 0.0902\n",
      "2/16, train_loss: 0.0701\n",
      "3/16, train_loss: 0.1851\n",
      "4/16, train_loss: 0.1001\n",
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      "16/16, train_loss: 0.1055\n",
      "17/16, train_loss: 0.0917\n",
      "epoch 150 average loss: 0.1085\n",
      "saved new best metric model at the 150th epoch\n",
      "current epoch: 150 current mean dice: 0.8330 \n",
      "best mean dice: 0.8330  at epoch: 150\n",
      "----------\n",
      "epoch 151/600\n",
      "1/16, train_loss: 0.0799\n",
      "2/16, train_loss: 0.1180\n",
      "3/16, train_loss: 0.1317\n",
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      "11/16, train_loss: 0.1238\n",
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      "14/16, train_loss: 0.1048\n",
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      "16/16, train_loss: 0.1124\n",
      "17/16, train_loss: 0.1625\n",
      "epoch 151 average loss: 0.1063\n",
      "----------\n",
      "epoch 152/600\n",
      "1/16, train_loss: 0.0681\n",
      "2/16, train_loss: 0.0681\n",
      "3/16, train_loss: 0.1863\n",
      "4/16, train_loss: 0.0837\n",
      "5/16, train_loss: 0.0999\n",
      "6/16, train_loss: 0.1057\n",
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      "11/16, train_loss: 0.1408\n",
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      "14/16, train_loss: 0.0857\n",
      "15/16, train_loss: 0.1460\n",
      "16/16, train_loss: 0.0980\n",
      "17/16, train_loss: 0.0983\n",
      "epoch 152 average loss: 0.1074\n",
      "----------\n",
      "epoch 153/600\n",
      "1/16, train_loss: 0.0889\n",
      "2/16, train_loss: 0.0799\n",
      "3/16, train_loss: 0.0893\n",
      "4/16, train_loss: 0.1092\n",
      "5/16, train_loss: 0.1061\n",
      "6/16, train_loss: 0.0960\n",
      "7/16, train_loss: 0.0997\n",
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      "9/16, train_loss: 0.1000\n",
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      "11/16, train_loss: 0.1312\n",
      "12/16, train_loss: 0.0851\n",
      "13/16, train_loss: 0.0882\n",
      "14/16, train_loss: 0.1131\n",
      "15/16, train_loss: 0.0949\n",
      "16/16, train_loss: 0.1050\n",
      "17/16, train_loss: 0.0847\n",
      "epoch 153 average loss: 0.0971\n",
      "----------\n",
      "epoch 154/600\n",
      "1/16, train_loss: 0.0912\n",
      "2/16, train_loss: 0.0723\n",
      "3/16, train_loss: 0.1825\n",
      "4/16, train_loss: 0.1243\n",
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      "9/16, train_loss: 0.0739\n",
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      "11/16, train_loss: 0.0922\n",
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      "14/16, train_loss: 0.1155\n",
      "15/16, train_loss: 0.1036\n",
      "16/16, train_loss: 0.0986\n",
      "17/16, train_loss: 0.0829\n",
      "epoch 154 average loss: 0.1010\n",
      "----------\n",
      "epoch 155/600\n",
      "1/16, train_loss: 0.0988\n",
      "2/16, train_loss: 0.0662\n",
      "3/16, train_loss: 0.1385\n",
      "4/16, train_loss: 0.1016\n",
      "5/16, train_loss: 0.1017\n",
      "6/16, train_loss: 0.0791\n",
      "7/16, train_loss: 0.1138\n",
      "8/16, train_loss: 0.0964\n",
      "9/16, train_loss: 0.1153\n",
      "10/16, train_loss: 0.0929\n",
      "11/16, train_loss: 0.0939\n",
      "12/16, train_loss: 0.1101\n",
      "13/16, train_loss: 0.0888\n",
      "14/16, train_loss: 0.1243\n",
      "15/16, train_loss: 0.1690\n",
      "16/16, train_loss: 0.0954\n",
      "17/16, train_loss: 0.1042\n",
      "epoch 155 average loss: 0.1053\n",
      "current epoch: 155 current mean dice: 0.8006 \n",
      "best mean dice: 0.8330  at epoch: 150\n",
      "----------\n",
      "epoch 156/600\n",
      "1/16, train_loss: 0.1148\n",
      "2/16, train_loss: 0.0792\n",
      "3/16, train_loss: 0.1189\n",
      "4/16, train_loss: 0.0962\n",
      "5/16, train_loss: 0.0995\n",
      "6/16, train_loss: 0.1125\n",
      "7/16, train_loss: 0.0927\n",
      "8/16, train_loss: 0.0894\n",
      "9/16, train_loss: 0.0882\n",
      "10/16, train_loss: 0.1110\n",
      "11/16, train_loss: 0.1227\n",
      "12/16, train_loss: 0.0894\n",
      "13/16, train_loss: 0.1252\n",
      "14/16, train_loss: 0.1469\n",
      "15/16, train_loss: 0.1789\n",
      "16/16, train_loss: 0.0831\n",
      "17/16, train_loss: 0.0820\n",
      "epoch 156 average loss: 0.1077\n",
      "----------\n",
      "epoch 157/600\n",
      "1/16, train_loss: 0.0736\n",
      "2/16, train_loss: 0.0900\n",
      "3/16, train_loss: 0.1250\n",
      "4/16, train_loss: 0.0835\n",
      "5/16, train_loss: 0.0924\n",
      "6/16, train_loss: 0.1336\n",
      "7/16, train_loss: 0.0864\n",
      "8/16, train_loss: 0.1191\n",
      "9/16, train_loss: 0.0636\n",
      "10/16, train_loss: 0.0954\n",
      "11/16, train_loss: 0.1444\n",
      "12/16, train_loss: 0.1203\n",
      "13/16, train_loss: 0.1029\n",
      "14/16, train_loss: 0.1758\n",
      "15/16, train_loss: 0.1140\n",
      "16/16, train_loss: 0.0956\n",
      "17/16, train_loss: 0.0961\n",
      "epoch 157 average loss: 0.1066\n",
      "----------\n",
      "epoch 158/600\n",
      "1/16, train_loss: 0.0912\n",
      "2/16, train_loss: 0.0767\n",
      "3/16, train_loss: 0.0736\n",
      "4/16, train_loss: 0.0983\n",
      "5/16, train_loss: 0.0932\n",
      "6/16, train_loss: 0.1234\n",
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      "8/16, train_loss: 0.1065\n",
      "9/16, train_loss: 0.0782\n",
      "10/16, train_loss: 0.0805\n",
      "11/16, train_loss: 0.1059\n",
      "12/16, train_loss: 0.1091\n",
      "13/16, train_loss: 0.0782\n",
      "14/16, train_loss: 0.1090\n",
      "15/16, train_loss: 0.1013\n",
      "16/16, train_loss: 0.0983\n",
      "17/16, train_loss: 0.0745\n",
      "epoch 158 average loss: 0.0970\n",
      "----------\n",
      "epoch 159/600\n",
      "1/16, train_loss: 0.0730\n",
      "2/16, train_loss: 0.0846\n",
      "3/16, train_loss: 0.0916\n",
      "4/16, train_loss: 0.0966\n",
      "5/16, train_loss: 0.1088\n",
      "6/16, train_loss: 0.1109\n",
      "7/16, train_loss: 0.0952\n",
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      "10/16, train_loss: 0.1381\n",
      "11/16, train_loss: 0.0995\n",
      "12/16, train_loss: 0.1136\n",
      "13/16, train_loss: 0.1186\n",
      "14/16, train_loss: 0.1050\n",
      "15/16, train_loss: 0.0970\n",
      "16/16, train_loss: 0.0896\n",
      "17/16, train_loss: 0.1000\n",
      "epoch 159 average loss: 0.0996\n",
      "----------\n",
      "epoch 160/600\n",
      "1/16, train_loss: 0.0707\n",
      "2/16, train_loss: 0.0898\n",
      "3/16, train_loss: 0.0802\n",
      "4/16, train_loss: 0.0855\n",
      "5/16, train_loss: 0.0924\n",
      "6/16, train_loss: 0.1030\n",
      "7/16, train_loss: 0.0724\n",
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      "11/16, train_loss: 0.1023\n",
      "12/16, train_loss: 0.1160\n",
      "13/16, train_loss: 0.0950\n",
      "14/16, train_loss: 0.0970\n",
      "15/16, train_loss: 0.1045\n",
      "16/16, train_loss: 0.0917\n",
      "17/16, train_loss: 0.0866\n",
      "epoch 160 average loss: 0.0899\n",
      "current epoch: 160 current mean dice: 0.8133 \n",
      "best mean dice: 0.8330  at epoch: 150\n",
      "----------\n",
      "epoch 161/600\n",
      "1/16, train_loss: 0.0652\n",
      "2/16, train_loss: 0.0642\n",
      "3/16, train_loss: 0.1104\n",
      "4/16, train_loss: 0.0815\n",
      "5/16, train_loss: 0.0794\n",
      "6/16, train_loss: 0.1007\n",
      "7/16, train_loss: 0.0947\n",
      "8/16, train_loss: 0.0855\n",
      "9/16, train_loss: 0.0746\n",
      "10/16, train_loss: 0.0902\n",
      "11/16, train_loss: 0.0914\n",
      "12/16, train_loss: 0.0806\n",
      "13/16, train_loss: 0.0980\n",
      "14/16, train_loss: 0.1294\n",
      "15/16, train_loss: 0.0963\n",
      "16/16, train_loss: 0.0866\n",
      "17/16, train_loss: 0.0869\n",
      "epoch 161 average loss: 0.0892\n",
      "----------\n",
      "epoch 162/600\n",
      "1/16, train_loss: 0.0741\n",
      "2/16, train_loss: 0.0666\n",
      "3/16, train_loss: 0.1591\n",
      "4/16, train_loss: 0.1041\n",
      "5/16, train_loss: 0.0747\n",
      "6/16, train_loss: 0.1224\n",
      "7/16, train_loss: 0.0917\n",
      "8/16, train_loss: 0.0864\n",
      "9/16, train_loss: 0.1103\n",
      "10/16, train_loss: 0.1061\n",
      "11/16, train_loss: 0.0844\n",
      "12/16, train_loss: 0.1030\n",
      "13/16, train_loss: 0.0993\n",
      "14/16, train_loss: 0.1154\n",
      "15/16, train_loss: 0.1289\n",
      "16/16, train_loss: 0.0926\n",
      "17/16, train_loss: 0.0519\n",
      "epoch 162 average loss: 0.0983\n",
      "----------\n",
      "epoch 163/600\n",
      "1/16, train_loss: 0.0829\n",
      "2/16, train_loss: 0.0690\n",
      "3/16, train_loss: 0.1107\n",
      "4/16, train_loss: 0.0777\n",
      "5/16, train_loss: 0.1092\n",
      "6/16, train_loss: 0.0905\n",
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      "11/16, train_loss: 0.1052\n",
      "12/16, train_loss: 0.0906\n",
      "13/16, train_loss: 0.1023\n",
      "14/16, train_loss: 0.0909\n",
      "15/16, train_loss: 0.1019\n",
      "16/16, train_loss: 0.1030\n",
      "17/16, train_loss: 0.0859\n",
      "epoch 163 average loss: 0.0937\n",
      "----------\n",
      "epoch 164/600\n",
      "1/16, train_loss: 0.0722\n",
      "2/16, train_loss: 0.0799\n",
      "3/16, train_loss: 0.2183\n",
      "4/16, train_loss: 0.1111\n",
      "5/16, train_loss: 0.1108\n",
      "6/16, train_loss: 0.1020\n",
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      "11/16, train_loss: 0.1556\n",
      "12/16, train_loss: 0.1055\n",
      "13/16, train_loss: 0.0954\n",
      "14/16, train_loss: 0.0757\n",
      "15/16, train_loss: 0.1166\n",
      "16/16, train_loss: 0.0860\n",
      "17/16, train_loss: 0.0884\n",
      "epoch 164 average loss: 0.1100\n",
      "----------\n",
      "epoch 165/600\n",
      "1/16, train_loss: 0.0804\n",
      "2/16, train_loss: 0.0679\n",
      "3/16, train_loss: 0.1169\n",
      "4/16, train_loss: 0.0924\n",
      "5/16, train_loss: 0.0974\n",
      "6/16, train_loss: 0.1333\n",
      "7/16, train_loss: 0.0885\n",
      "8/16, train_loss: 0.1166\n",
      "9/16, train_loss: 0.0820\n",
      "10/16, train_loss: 0.1027\n",
      "11/16, train_loss: 0.1128\n",
      "12/16, train_loss: 0.1106\n",
      "13/16, train_loss: 0.0838\n",
      "14/16, train_loss: 0.1058\n",
      "15/16, train_loss: 0.0887\n",
      "16/16, train_loss: 0.1032\n",
      "17/16, train_loss: 0.0497\n",
      "epoch 165 average loss: 0.0960\n",
      "current epoch: 165 current mean dice: 0.8186 \n",
      "best mean dice: 0.8330  at epoch: 150\n",
      "----------\n",
      "epoch 166/600\n",
      "1/16, train_loss: 0.0793\n",
      "2/16, train_loss: 0.0910\n",
      "3/16, train_loss: 0.0848\n",
      "4/16, train_loss: 0.1011\n",
      "5/16, train_loss: 0.0737\n",
      "6/16, train_loss: 0.0831\n",
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      "9/16, train_loss: 0.0746\n",
      "10/16, train_loss: 0.0993\n",
      "11/16, train_loss: 0.0925\n",
      "12/16, train_loss: 0.1136\n",
      "13/16, train_loss: 0.1211\n",
      "14/16, train_loss: 0.0834\n",
      "15/16, train_loss: 0.1011\n",
      "16/16, train_loss: 0.0965\n",
      "17/16, train_loss: 0.0762\n",
      "epoch 166 average loss: 0.0965\n",
      "----------\n",
      "epoch 167/600\n",
      "1/16, train_loss: 0.0945\n",
      "2/16, train_loss: 0.0727\n",
      "3/16, train_loss: 0.1214\n",
      "4/16, train_loss: 0.0965\n",
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      "6/16, train_loss: 0.0931\n",
      "7/16, train_loss: 0.0999\n",
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      "9/16, train_loss: 0.0811\n",
      "10/16, train_loss: 0.1130\n",
      "11/16, train_loss: 0.0729\n",
      "12/16, train_loss: 0.1074\n",
      "13/16, train_loss: 0.0941\n",
      "14/16, train_loss: 0.1170\n",
      "15/16, train_loss: 0.1325\n",
      "16/16, train_loss: 0.1151\n",
      "17/16, train_loss: 0.0982\n",
      "epoch 167 average loss: 0.1037\n",
      "----------\n",
      "epoch 168/600\n",
      "1/16, train_loss: 0.0767\n",
      "2/16, train_loss: 0.0707\n",
      "3/16, train_loss: 0.1974\n",
      "4/16, train_loss: 0.0835\n",
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      "6/16, train_loss: 0.0902\n",
      "7/16, train_loss: 0.0957\n",
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      "11/16, train_loss: 0.1227\n",
      "12/16, train_loss: 0.1198\n",
      "13/16, train_loss: 0.0822\n",
      "14/16, train_loss: 0.1456\n",
      "15/16, train_loss: 0.1137\n",
      "16/16, train_loss: 0.0898\n",
      "17/16, train_loss: 0.0930\n",
      "epoch 168 average loss: 0.1029\n",
      "----------\n",
      "epoch 169/600\n",
      "1/16, train_loss: 0.0951\n",
      "2/16, train_loss: 0.0792\n",
      "3/16, train_loss: 0.1240\n",
      "4/16, train_loss: 0.0750\n",
      "5/16, train_loss: 0.1003\n",
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      "16/16, train_loss: 0.0897\n",
      "17/16, train_loss: 0.1561\n",
      "epoch 169 average loss: 0.1026\n",
      "----------\n",
      "epoch 170/600\n",
      "1/16, train_loss: 0.0567\n",
      "2/16, train_loss: 0.0822\n",
      "3/16, train_loss: 0.0869\n",
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      "16/16, train_loss: 0.1050\n",
      "17/16, train_loss: 0.0957\n",
      "epoch 170 average loss: 0.0983\n",
      "saved new best metric model at the 170th epoch\n",
      "current epoch: 170 current mean dice: 0.8406 \n",
      "best mean dice: 0.8406  at epoch: 170\n",
      "----------\n",
      "epoch 171/600\n",
      "1/16, train_loss: 0.1005\n",
      "2/16, train_loss: 0.0604\n",
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      "epoch 171 average loss: 0.1035\n",
      "----------\n",
      "epoch 172/600\n",
      "1/16, train_loss: 0.0845\n",
      "2/16, train_loss: 0.0759\n",
      "3/16, train_loss: 0.1201\n",
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      "15/16, train_loss: 0.0879\n",
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      "17/16, train_loss: 0.0985\n",
      "epoch 172 average loss: 0.0958\n",
      "----------\n",
      "epoch 173/600\n",
      "1/16, train_loss: 0.1029\n",
      "2/16, train_loss: 0.0930\n",
      "3/16, train_loss: 0.0830\n",
      "4/16, train_loss: 0.1117\n",
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      "15/16, train_loss: 0.0919\n",
      "16/16, train_loss: 0.0892\n",
      "17/16, train_loss: 0.1033\n",
      "epoch 173 average loss: 0.0958\n",
      "----------\n",
      "epoch 174/600\n",
      "1/16, train_loss: 0.0716\n",
      "2/16, train_loss: 0.0691\n",
      "3/16, train_loss: 0.1168\n",
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      "11/16, train_loss: 0.0817\n",
      "12/16, train_loss: 0.0896\n",
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      "15/16, train_loss: 0.2109\n",
      "16/16, train_loss: 0.0993\n",
      "17/16, train_loss: 0.0900\n",
      "epoch 174 average loss: 0.0975\n",
      "----------\n",
      "epoch 175/600\n",
      "1/16, train_loss: 0.0885\n",
      "2/16, train_loss: 0.0664\n",
      "3/16, train_loss: 0.1083\n",
      "4/16, train_loss: 0.0789\n",
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      "6/16, train_loss: 0.1126\n",
      "7/16, train_loss: 0.1054\n",
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      "14/16, train_loss: 0.0957\n",
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      "16/16, train_loss: 0.1276\n",
      "17/16, train_loss: 0.2334\n",
      "epoch 175 average loss: 0.1054\n",
      "current epoch: 175 current mean dice: 0.8179 \n",
      "best mean dice: 0.8406  at epoch: 170\n",
      "----------\n",
      "epoch 176/600\n",
      "1/16, train_loss: 0.0746\n",
      "2/16, train_loss: 0.0778\n",
      "3/16, train_loss: 0.1915\n",
      "4/16, train_loss: 0.0972\n",
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      "11/16, train_loss: 0.0978\n",
      "12/16, train_loss: 0.0834\n",
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      "15/16, train_loss: 0.0849\n",
      "16/16, train_loss: 0.0753\n",
      "17/16, train_loss: 0.0624\n",
      "epoch 176 average loss: 0.0922\n",
      "----------\n",
      "epoch 177/600\n",
      "1/16, train_loss: 0.0797\n",
      "2/16, train_loss: 0.0718\n",
      "3/16, train_loss: 0.1562\n",
      "4/16, train_loss: 0.0889\n",
      "5/16, train_loss: 0.0869\n",
      "6/16, train_loss: 0.0835\n",
      "7/16, train_loss: 0.1002\n",
      "8/16, train_loss: 0.0850\n",
      "9/16, train_loss: 0.0639\n",
      "10/16, train_loss: 0.1005\n",
      "11/16, train_loss: 0.0857\n",
      "12/16, train_loss: 0.0826\n",
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      "14/16, train_loss: 0.0785\n",
      "15/16, train_loss: 0.0910\n",
      "16/16, train_loss: 0.0872\n",
      "17/16, train_loss: 0.0538\n",
      "epoch 177 average loss: 0.0869\n",
      "----------\n",
      "epoch 178/600\n",
      "1/16, train_loss: 0.0775\n",
      "2/16, train_loss: 0.0628\n",
      "3/16, train_loss: 0.1190\n",
      "4/16, train_loss: 0.0740\n",
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      "6/16, train_loss: 0.0928\n",
      "7/16, train_loss: 0.0739\n",
      "8/16, train_loss: 0.0882\n",
      "9/16, train_loss: 0.0656\n",
      "10/16, train_loss: 0.1072\n",
      "11/16, train_loss: 0.0842\n",
      "12/16, train_loss: 0.1094\n",
      "13/16, train_loss: 0.1005\n",
      "14/16, train_loss: 0.0855\n",
      "15/16, train_loss: 0.0860\n",
      "16/16, train_loss: 0.0706\n",
      "17/16, train_loss: 0.0703\n",
      "epoch 178 average loss: 0.0850\n",
      "----------\n",
      "epoch 179/600\n",
      "1/16, train_loss: 0.0770\n",
      "2/16, train_loss: 0.0723\n",
      "3/16, train_loss: 0.0912\n",
      "4/16, train_loss: 0.0815\n",
      "5/16, train_loss: 0.0987\n",
      "6/16, train_loss: 0.0808\n",
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      "9/16, train_loss: 0.0778\n",
      "10/16, train_loss: 0.0894\n",
      "11/16, train_loss: 0.1093\n",
      "12/16, train_loss: 0.1144\n",
      "13/16, train_loss: 0.1024\n",
      "14/16, train_loss: 0.0855\n",
      "15/16, train_loss: 0.0946\n",
      "16/16, train_loss: 0.0862\n",
      "17/16, train_loss: 0.0554\n",
      "epoch 179 average loss: 0.0871\n",
      "----------\n",
      "epoch 180/600\n",
      "1/16, train_loss: 0.1367\n",
      "2/16, train_loss: 0.0592\n",
      "3/16, train_loss: 0.1241\n",
      "4/16, train_loss: 0.0813\n",
      "5/16, train_loss: 0.0883\n",
      "6/16, train_loss: 0.1098\n",
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      "11/16, train_loss: 0.1655\n",
      "12/16, train_loss: 0.0780\n",
      "13/16, train_loss: 0.1142\n",
      "14/16, train_loss: 0.1237\n",
      "15/16, train_loss: 0.1187\n",
      "16/16, train_loss: 0.0975\n",
      "17/16, train_loss: 0.0936\n",
      "epoch 180 average loss: 0.1015\n",
      "current epoch: 180 current mean dice: 0.8394 \n",
      "best mean dice: 0.8406  at epoch: 170\n",
      "----------\n",
      "epoch 181/600\n",
      "1/16, train_loss: 0.0669\n",
      "2/16, train_loss: 0.0721\n",
      "3/16, train_loss: 0.2297\n",
      "4/16, train_loss: 0.1077\n",
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      "11/16, train_loss: 0.0941\n",
      "12/16, train_loss: 0.1033\n",
      "13/16, train_loss: 0.0824\n",
      "14/16, train_loss: 0.0760\n",
      "15/16, train_loss: 0.1030\n",
      "16/16, train_loss: 0.0825\n",
      "17/16, train_loss: 0.0864\n",
      "epoch 181 average loss: 0.0971\n",
      "----------\n",
      "epoch 182/600\n",
      "1/16, train_loss: 0.0708\n",
      "2/16, train_loss: 0.0706\n",
      "3/16, train_loss: 0.1211\n",
      "4/16, train_loss: 0.0837\n",
      "5/16, train_loss: 0.0799\n",
      "6/16, train_loss: 0.1047\n",
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      "10/16, train_loss: 0.0886\n",
      "11/16, train_loss: 0.1239\n",
      "12/16, train_loss: 0.1022\n",
      "13/16, train_loss: 0.0905\n",
      "14/16, train_loss: 0.1190\n",
      "15/16, train_loss: 0.0881\n",
      "16/16, train_loss: 0.0787\n",
      "17/16, train_loss: 0.0527\n",
      "epoch 182 average loss: 0.0895\n",
      "----------\n",
      "epoch 183/600\n",
      "1/16, train_loss: 0.0709\n",
      "2/16, train_loss: 0.0771\n",
      "3/16, train_loss: 0.1513\n",
      "4/16, train_loss: 0.0811\n",
      "5/16, train_loss: 0.0698\n",
      "6/16, train_loss: 0.1022\n",
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      "9/16, train_loss: 0.0699\n",
      "10/16, train_loss: 0.1017\n",
      "11/16, train_loss: 0.1150\n",
      "12/16, train_loss: 0.0952\n",
      "13/16, train_loss: 0.0885\n",
      "14/16, train_loss: 0.0814\n",
      "15/16, train_loss: 0.1055\n",
      "16/16, train_loss: 0.0850\n",
      "17/16, train_loss: 0.2266\n",
      "epoch 183 average loss: 0.1001\n",
      "----------\n",
      "epoch 184/600\n",
      "1/16, train_loss: 0.1035\n",
      "2/16, train_loss: 0.0715\n",
      "3/16, train_loss: 0.0875\n",
      "4/16, train_loss: 0.0841\n",
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      "12/16, train_loss: 0.0836\n",
      "13/16, train_loss: 0.0865\n",
      "14/16, train_loss: 0.0761\n",
      "15/16, train_loss: 0.0915\n",
      "16/16, train_loss: 0.0883\n",
      "17/16, train_loss: 0.0811\n",
      "epoch 184 average loss: 0.0870\n",
      "----------\n",
      "epoch 185/600\n",
      "1/16, train_loss: 0.0852\n",
      "2/16, train_loss: 0.0652\n",
      "3/16, train_loss: 0.1165\n",
      "4/16, train_loss: 0.1203\n",
      "5/16, train_loss: 0.0714\n",
      "6/16, train_loss: 0.1021\n",
      "7/16, train_loss: 0.0982\n",
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      "10/16, train_loss: 0.1045\n",
      "11/16, train_loss: 0.0889\n",
      "12/16, train_loss: 0.1139\n",
      "13/16, train_loss: 0.0831\n",
      "14/16, train_loss: 0.0965\n",
      "15/16, train_loss: 0.0791\n",
      "16/16, train_loss: 0.0874\n",
      "17/16, train_loss: 0.0736\n",
      "epoch 185 average loss: 0.0901\n",
      "current epoch: 185 current mean dice: 0.8102 \n",
      "best mean dice: 0.8406  at epoch: 170\n",
      "----------\n",
      "epoch 186/600\n",
      "1/16, train_loss: 0.1136\n",
      "2/16, train_loss: 0.0685\n",
      "3/16, train_loss: 0.1153\n",
      "4/16, train_loss: 0.0724\n",
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      "11/16, train_loss: 0.0996\n",
      "12/16, train_loss: 0.1069\n",
      "13/16, train_loss: 0.0773\n",
      "14/16, train_loss: 0.1196\n",
      "15/16, train_loss: 0.0827\n",
      "16/16, train_loss: 0.0713\n",
      "17/16, train_loss: 0.1059\n",
      "epoch 186 average loss: 0.0873\n",
      "----------\n",
      "epoch 187/600\n",
      "1/16, train_loss: 0.0606\n",
      "2/16, train_loss: 0.0768\n",
      "3/16, train_loss: 0.0706\n",
      "4/16, train_loss: 0.1023\n",
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      "6/16, train_loss: 0.1140\n",
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      "12/16, train_loss: 0.1064\n",
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      "14/16, train_loss: 0.0853\n",
      "15/16, train_loss: 0.0847\n",
      "16/16, train_loss: 0.0872\n",
      "17/16, train_loss: 0.1192\n",
      "epoch 187 average loss: 0.0930\n",
      "----------\n",
      "epoch 188/600\n",
      "1/16, train_loss: 0.1179\n",
      "2/16, train_loss: 0.0642\n",
      "3/16, train_loss: 0.1070\n",
      "4/16, train_loss: 0.0909\n",
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      "11/16, train_loss: 0.0827\n",
      "12/16, train_loss: 0.0834\n",
      "13/16, train_loss: 0.0840\n",
      "14/16, train_loss: 0.1247\n",
      "15/16, train_loss: 0.0918\n",
      "16/16, train_loss: 0.0872\n",
      "17/16, train_loss: 0.0552\n",
      "epoch 188 average loss: 0.0883\n",
      "----------\n",
      "epoch 189/600\n",
      "1/16, train_loss: 0.0939\n",
      "2/16, train_loss: 0.0715\n",
      "3/16, train_loss: 0.0837\n",
      "4/16, train_loss: 0.1236\n",
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      "11/16, train_loss: 0.1228\n",
      "12/16, train_loss: 0.1147\n",
      "13/16, train_loss: 0.1075\n",
      "14/16, train_loss: 0.1016\n",
      "15/16, train_loss: 0.1197\n",
      "16/16, train_loss: 0.0947\n",
      "17/16, train_loss: 0.0893\n",
      "epoch 189 average loss: 0.0974\n",
      "----------\n",
      "epoch 190/600\n",
      "1/16, train_loss: 0.0773\n",
      "2/16, train_loss: 0.0638\n",
      "3/16, train_loss: 0.0804\n",
      "4/16, train_loss: 0.0881\n",
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      "11/16, train_loss: 0.0892\n",
      "12/16, train_loss: 0.1005\n",
      "13/16, train_loss: 0.1010\n",
      "14/16, train_loss: 0.0803\n",
      "15/16, train_loss: 0.0841\n",
      "16/16, train_loss: 0.0902\n",
      "17/16, train_loss: 0.0923\n",
      "epoch 190 average loss: 0.0869\n",
      "current epoch: 190 current mean dice: 0.8266 \n",
      "best mean dice: 0.8406  at epoch: 170\n",
      "----------\n",
      "epoch 191/600\n",
      "1/16, train_loss: 0.1047\n",
      "2/16, train_loss: 0.0580\n",
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      "6/16, train_loss: 0.0917\n",
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      "16/16, train_loss: 0.0827\n",
      "17/16, train_loss: 0.0574\n",
      "epoch 191 average loss: 0.0925\n",
      "----------\n",
      "epoch 192/600\n",
      "1/16, train_loss: 0.0735\n",
      "2/16, train_loss: 0.0699\n",
      "3/16, train_loss: 0.1091\n",
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      "epoch 192 average loss: 0.0937\n",
      "----------\n",
      "epoch 193/600\n",
      "1/16, train_loss: 0.1032\n",
      "2/16, train_loss: 0.0653\n",
      "3/16, train_loss: 0.0926\n",
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      "16/16, train_loss: 0.0934\n",
      "17/16, train_loss: 0.1509\n",
      "epoch 193 average loss: 0.0901\n",
      "----------\n",
      "epoch 194/600\n",
      "1/16, train_loss: 0.0826\n",
      "2/16, train_loss: 0.0678\n",
      "3/16, train_loss: 0.0968\n",
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      "17/16, train_loss: 0.0615\n",
      "epoch 194 average loss: 0.0806\n",
      "----------\n",
      "epoch 195/600\n",
      "1/16, train_loss: 0.0849\n",
      "2/16, train_loss: 0.0673\n",
      "3/16, train_loss: 0.1552\n",
      "4/16, train_loss: 0.0782\n",
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      "7/16, train_loss: 0.0815\n",
      "8/16, train_loss: 0.0894\n",
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      "10/16, train_loss: 0.0934\n",
      "11/16, train_loss: 0.0672\n",
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      "15/16, train_loss: 0.0735\n",
      "16/16, train_loss: 0.0792\n",
      "17/16, train_loss: 0.0651\n",
      "epoch 195 average loss: 0.0897\n",
      "current epoch: 195 current mean dice: 0.8272 \n",
      "best mean dice: 0.8406  at epoch: 170\n",
      "----------\n",
      "epoch 196/600\n",
      "1/16, train_loss: 0.0552\n",
      "2/16, train_loss: 0.0758\n",
      "3/16, train_loss: 0.0960\n",
      "4/16, train_loss: 0.0811\n",
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      "11/16, train_loss: 0.1373\n",
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      "15/16, train_loss: 0.0815\n",
      "16/16, train_loss: 0.0711\n",
      "17/16, train_loss: 0.1959\n",
      "epoch 196 average loss: 0.0923\n",
      "----------\n",
      "epoch 197/600\n",
      "1/16, train_loss: 0.0952\n",
      "2/16, train_loss: 0.0711\n",
      "3/16, train_loss: 0.1575\n",
      "4/16, train_loss: 0.0740\n",
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      "6/16, train_loss: 0.1199\n",
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      "15/16, train_loss: 0.0779\n",
      "16/16, train_loss: 0.0906\n",
      "17/16, train_loss: 0.0896\n",
      "epoch 197 average loss: 0.0993\n",
      "----------\n",
      "epoch 198/600\n",
      "1/16, train_loss: 0.0814\n",
      "2/16, train_loss: 0.1066\n",
      "3/16, train_loss: 0.1164\n",
      "4/16, train_loss: 0.0825\n",
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      "6/16, train_loss: 0.0935\n",
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      "9/16, train_loss: 0.1186\n",
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      "13/16, train_loss: 0.0863\n",
      "14/16, train_loss: 0.0829\n",
      "15/16, train_loss: 0.0908\n",
      "16/16, train_loss: 0.1106\n",
      "17/16, train_loss: 0.1007\n",
      "epoch 198 average loss: 0.0960\n",
      "----------\n",
      "epoch 199/600\n",
      "1/16, train_loss: 0.0646\n",
      "2/16, train_loss: 0.0845\n",
      "3/16, train_loss: 0.0857\n",
      "4/16, train_loss: 0.0800\n",
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      "11/16, train_loss: 0.1167\n",
      "12/16, train_loss: 0.0995\n",
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      "14/16, train_loss: 0.0697\n",
      "15/16, train_loss: 0.0648\n",
      "16/16, train_loss: 0.0928\n",
      "17/16, train_loss: 0.1232\n",
      "epoch 199 average loss: 0.0915\n",
      "----------\n",
      "epoch 200/600\n",
      "1/16, train_loss: 0.0751\n",
      "2/16, train_loss: 0.0659\n",
      "3/16, train_loss: 0.1179\n",
      "4/16, train_loss: 0.1228\n",
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      "6/16, train_loss: 0.0814\n",
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      "8/16, train_loss: 0.0846\n",
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      "10/16, train_loss: 0.0695\n",
      "11/16, train_loss: 0.1014\n",
      "12/16, train_loss: 0.1116\n",
      "13/16, train_loss: 0.0860\n",
      "14/16, train_loss: 0.0894\n",
      "15/16, train_loss: 0.0660\n",
      "16/16, train_loss: 0.0844\n",
      "17/16, train_loss: 0.0580\n",
      "epoch 200 average loss: 0.0866\n",
      "saved new best metric model at the 200th epoch\n",
      "current epoch: 200 current mean dice: 0.8423 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 201/600\n",
      "1/16, train_loss: 0.0927\n",
      "2/16, train_loss: 0.0721\n",
      "3/16, train_loss: 0.1242\n",
      "4/16, train_loss: 0.0906\n",
      "5/16, train_loss: 0.0863\n",
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      "11/16, train_loss: 0.0872\n",
      "12/16, train_loss: 0.1227\n",
      "13/16, train_loss: 0.0856\n",
      "14/16, train_loss: 0.0847\n",
      "15/16, train_loss: 0.0982\n",
      "16/16, train_loss: 0.0725\n",
      "17/16, train_loss: 0.1323\n",
      "epoch 201 average loss: 0.0911\n",
      "----------\n",
      "epoch 202/600\n",
      "1/16, train_loss: 0.0711\n",
      "2/16, train_loss: 0.0657\n",
      "3/16, train_loss: 0.1682\n",
      "4/16, train_loss: 0.0856\n",
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      "11/16, train_loss: 0.0773\n",
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      "14/16, train_loss: 0.0913\n",
      "15/16, train_loss: 0.0781\n",
      "16/16, train_loss: 0.0925\n",
      "17/16, train_loss: 0.0855\n",
      "epoch 202 average loss: 0.0853\n",
      "----------\n",
      "epoch 203/600\n",
      "1/16, train_loss: 0.0615\n",
      "2/16, train_loss: 0.0664\n",
      "3/16, train_loss: 0.0804\n",
      "4/16, train_loss: 0.0807\n",
      "5/16, train_loss: 0.0792\n",
      "6/16, train_loss: 0.0894\n",
      "7/16, train_loss: 0.0886\n",
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      "11/16, train_loss: 0.1134\n",
      "12/16, train_loss: 0.1141\n",
      "13/16, train_loss: 0.0846\n",
      "14/16, train_loss: 0.0860\n",
      "15/16, train_loss: 0.1018\n",
      "16/16, train_loss: 0.0879\n",
      "17/16, train_loss: 0.0599\n",
      "epoch 203 average loss: 0.0852\n",
      "----------\n",
      "epoch 204/600\n",
      "1/16, train_loss: 0.1059\n",
      "2/16, train_loss: 0.0540\n",
      "3/16, train_loss: 0.1190\n",
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      "14/16, train_loss: 0.1012\n",
      "15/16, train_loss: 0.0894\n",
      "16/16, train_loss: 0.0924\n",
      "17/16, train_loss: 0.0829\n",
      "epoch 204 average loss: 0.0892\n",
      "----------\n",
      "epoch 205/600\n",
      "1/16, train_loss: 0.0850\n",
      "2/16, train_loss: 0.0677\n",
      "3/16, train_loss: 0.0795\n",
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      "11/16, train_loss: 0.0871\n",
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      "13/16, train_loss: 0.0843\n",
      "14/16, train_loss: 0.1186\n",
      "15/16, train_loss: 0.0771\n",
      "16/16, train_loss: 0.0839\n",
      "17/16, train_loss: 0.0637\n",
      "epoch 205 average loss: 0.0847\n",
      "current epoch: 205 current mean dice: 0.8196 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 206/600\n",
      "1/16, train_loss: 0.0675\n",
      "2/16, train_loss: 0.0795\n",
      "3/16, train_loss: 0.0724\n",
      "4/16, train_loss: 0.0793\n",
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      "11/16, train_loss: 0.1060\n",
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      "14/16, train_loss: 0.0785\n",
      "15/16, train_loss: 0.1258\n",
      "16/16, train_loss: 0.0857\n",
      "17/16, train_loss: 0.0857\n",
      "epoch 206 average loss: 0.0867\n",
      "----------\n",
      "epoch 207/600\n",
      "1/16, train_loss: 0.0728\n",
      "2/16, train_loss: 0.0661\n",
      "3/16, train_loss: 0.1620\n",
      "4/16, train_loss: 0.0833\n",
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      "11/16, train_loss: 0.0689\n",
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      "14/16, train_loss: 0.0980\n",
      "15/16, train_loss: 0.0829\n",
      "16/16, train_loss: 0.0583\n",
      "17/16, train_loss: 0.0793\n",
      "epoch 207 average loss: 0.0843\n",
      "----------\n",
      "epoch 208/600\n",
      "1/16, train_loss: 0.0687\n",
      "2/16, train_loss: 0.0607\n",
      "3/16, train_loss: 0.1042\n",
      "4/16, train_loss: 0.1224\n",
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      "11/16, train_loss: 0.0904\n",
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      "14/16, train_loss: 0.0929\n",
      "15/16, train_loss: 0.0868\n",
      "16/16, train_loss: 0.0817\n",
      "17/16, train_loss: 0.0985\n",
      "epoch 208 average loss: 0.0849\n",
      "----------\n",
      "epoch 209/600\n",
      "1/16, train_loss: 0.1533\n",
      "2/16, train_loss: 0.0617\n",
      "3/16, train_loss: 0.1019\n",
      "4/16, train_loss: 0.0898\n",
      "5/16, train_loss: 0.1148\n",
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      "11/16, train_loss: 0.0966\n",
      "12/16, train_loss: 0.1152\n",
      "13/16, train_loss: 0.0974\n",
      "14/16, train_loss: 0.0889\n",
      "15/16, train_loss: 0.0886\n",
      "16/16, train_loss: 0.0919\n",
      "17/16, train_loss: 0.0617\n",
      "epoch 209 average loss: 0.0930\n",
      "----------\n",
      "epoch 210/600\n",
      "1/16, train_loss: 0.0883\n",
      "2/16, train_loss: 0.0530\n",
      "3/16, train_loss: 0.1666\n",
      "4/16, train_loss: 0.0931\n",
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      "11/16, train_loss: 0.1246\n",
      "12/16, train_loss: 0.0929\n",
      "13/16, train_loss: 0.0893\n",
      "14/16, train_loss: 0.0828\n",
      "15/16, train_loss: 0.0878\n",
      "16/16, train_loss: 0.0724\n",
      "17/16, train_loss: 0.0599\n",
      "epoch 210 average loss: 0.0920\n",
      "current epoch: 210 current mean dice: 0.8033 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 211/600\n",
      "1/16, train_loss: 0.0646\n",
      "2/16, train_loss: 0.0696\n",
      "3/16, train_loss: 0.0708\n",
      "4/16, train_loss: 0.0826\n",
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      "11/16, train_loss: 0.0689\n",
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      "13/16, train_loss: 0.0939\n",
      "14/16, train_loss: 0.1036\n",
      "15/16, train_loss: 0.0933\n",
      "16/16, train_loss: 0.1047\n",
      "17/16, train_loss: 0.0479\n",
      "epoch 211 average loss: 0.0817\n",
      "----------\n",
      "epoch 212/600\n",
      "1/16, train_loss: 0.0744\n",
      "2/16, train_loss: 0.0721\n",
      "3/16, train_loss: 0.0845\n",
      "4/16, train_loss: 0.0735\n",
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      "6/16, train_loss: 0.0888\n",
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      "10/16, train_loss: 0.0886\n",
      "11/16, train_loss: 0.0770\n",
      "12/16, train_loss: 0.1019\n",
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      "14/16, train_loss: 0.0846\n",
      "15/16, train_loss: 0.1117\n",
      "16/16, train_loss: 0.0837\n",
      "17/16, train_loss: 0.0976\n",
      "epoch 212 average loss: 0.0878\n",
      "----------\n",
      "epoch 213/600\n",
      "1/16, train_loss: 0.0916\n",
      "2/16, train_loss: 0.0576\n",
      "3/16, train_loss: 0.1010\n",
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      "8/16, train_loss: 0.0883\n",
      "9/16, train_loss: 0.0739\n",
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      "epoch 213 average loss: 0.0825\n",
      "----------\n",
      "epoch 214/600\n",
      "1/16, train_loss: 0.0658\n",
      "2/16, train_loss: 0.0667\n",
      "3/16, train_loss: 0.0848\n",
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      "epoch 214 average loss: 0.0808\n",
      "----------\n",
      "epoch 215/600\n",
      "1/16, train_loss: 0.0644\n",
      "2/16, train_loss: 0.0569\n",
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      "16/16, train_loss: 0.0943\n",
      "17/16, train_loss: 0.0660\n",
      "epoch 215 average loss: 0.0810\n",
      "current epoch: 215 current mean dice: 0.8249 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 216/600\n",
      "1/16, train_loss: 0.0771\n",
      "2/16, train_loss: 0.0533\n",
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      "epoch 216 average loss: 0.0875\n",
      "----------\n",
      "epoch 217/600\n",
      "1/16, train_loss: 0.0675\n",
      "2/16, train_loss: 0.0618\n",
      "3/16, train_loss: 0.0785\n",
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      "epoch 217 average loss: 0.0866\n",
      "----------\n",
      "epoch 218/600\n",
      "1/16, train_loss: 0.0769\n",
      "2/16, train_loss: 0.0662\n",
      "3/16, train_loss: 0.1136\n",
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      "17/16, train_loss: 0.0943\n",
      "epoch 218 average loss: 0.0856\n",
      "----------\n",
      "epoch 219/600\n",
      "1/16, train_loss: 0.0925\n",
      "2/16, train_loss: 0.0682\n",
      "3/16, train_loss: 0.0744\n",
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      "17/16, train_loss: 0.1189\n",
      "epoch 219 average loss: 0.0878\n",
      "----------\n",
      "epoch 220/600\n",
      "1/16, train_loss: 0.0579\n",
      "2/16, train_loss: 0.0710\n",
      "3/16, train_loss: 0.3063\n",
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      "11/16, train_loss: 0.0943\n",
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      "16/16, train_loss: 0.0827\n",
      "17/16, train_loss: 0.0535\n",
      "epoch 220 average loss: 0.1058\n",
      "current epoch: 220 current mean dice: 0.8354 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 221/600\n",
      "1/16, train_loss: 0.0624\n",
      "2/16, train_loss: 0.0606\n",
      "3/16, train_loss: 0.0840\n",
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      "15/16, train_loss: 0.0816\n",
      "16/16, train_loss: 0.0953\n",
      "17/16, train_loss: 0.0906\n",
      "epoch 221 average loss: 0.0954\n",
      "----------\n",
      "epoch 222/600\n",
      "1/16, train_loss: 0.0732\n",
      "2/16, train_loss: 0.0838\n",
      "3/16, train_loss: 0.1764\n",
      "4/16, train_loss: 0.0857\n",
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      "11/16, train_loss: 0.0836\n",
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      "16/16, train_loss: 0.0808\n",
      "17/16, train_loss: 0.0732\n",
      "epoch 222 average loss: 0.0907\n",
      "----------\n",
      "epoch 223/600\n",
      "1/16, train_loss: 0.0752\n",
      "2/16, train_loss: 0.0660\n",
      "3/16, train_loss: 0.0793\n",
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      "14/16, train_loss: 0.0878\n",
      "15/16, train_loss: 0.0718\n",
      "16/16, train_loss: 0.0894\n",
      "17/16, train_loss: 0.0807\n",
      "epoch 223 average loss: 0.0781\n",
      "----------\n",
      "epoch 224/600\n",
      "1/16, train_loss: 0.0513\n",
      "2/16, train_loss: 0.0772\n",
      "3/16, train_loss: 0.1116\n",
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      "11/16, train_loss: 0.1081\n",
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      "15/16, train_loss: 0.0809\n",
      "16/16, train_loss: 0.0741\n",
      "17/16, train_loss: 0.0779\n",
      "epoch 224 average loss: 0.0883\n",
      "----------\n",
      "epoch 225/600\n",
      "1/16, train_loss: 0.0616\n",
      "2/16, train_loss: 0.0606\n",
      "3/16, train_loss: 0.3186\n",
      "4/16, train_loss: 0.0758\n",
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      "15/16, train_loss: 0.0804\n",
      "16/16, train_loss: 0.0830\n",
      "17/16, train_loss: 0.0722\n",
      "epoch 225 average loss: 0.0955\n",
      "current epoch: 225 current mean dice: 0.8405 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 226/600\n",
      "1/16, train_loss: 0.0590\n",
      "2/16, train_loss: 0.0705\n",
      "3/16, train_loss: 0.1078\n",
      "4/16, train_loss: 0.0878\n",
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      "15/16, train_loss: 0.1166\n",
      "16/16, train_loss: 0.0871\n",
      "17/16, train_loss: 0.1010\n",
      "epoch 226 average loss: 0.0908\n",
      "----------\n",
      "epoch 227/600\n",
      "1/16, train_loss: 0.0736\n",
      "2/16, train_loss: 0.0591\n",
      "3/16, train_loss: 0.0837\n",
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      "16/16, train_loss: 0.0932\n",
      "17/16, train_loss: 0.0803\n",
      "epoch 227 average loss: 0.0820\n",
      "----------\n",
      "epoch 228/600\n",
      "1/16, train_loss: 0.0686\n",
      "2/16, train_loss: 0.0598\n",
      "3/16, train_loss: 0.0956\n",
      "4/16, train_loss: 0.0831\n",
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      "6/16, train_loss: 0.1080\n",
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      "16/16, train_loss: 0.0712\n",
      "17/16, train_loss: 0.0802\n",
      "epoch 228 average loss: 0.0851\n",
      "----------\n",
      "epoch 229/600\n",
      "1/16, train_loss: 0.0723\n",
      "2/16, train_loss: 0.0595\n",
      "3/16, train_loss: 0.0714\n",
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      "15/16, train_loss: 0.0719\n",
      "16/16, train_loss: 0.0845\n",
      "17/16, train_loss: 0.0752\n",
      "epoch 229 average loss: 0.0829\n",
      "----------\n",
      "epoch 230/600\n",
      "1/16, train_loss: 0.0813\n",
      "2/16, train_loss: 0.0621\n",
      "3/16, train_loss: 0.1080\n",
      "4/16, train_loss: 0.0768\n",
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      "11/16, train_loss: 0.0791\n",
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      "15/16, train_loss: 0.0890\n",
      "16/16, train_loss: 0.0792\n",
      "17/16, train_loss: 0.0492\n",
      "epoch 230 average loss: 0.0804\n",
      "current epoch: 230 current mean dice: 0.8366 \n",
      "best mean dice: 0.8423  at epoch: 200\n",
      "----------\n",
      "epoch 231/600\n",
      "1/16, train_loss: 0.0651\n",
      "2/16, train_loss: 0.0602\n",
      "3/16, train_loss: 0.0847\n",
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      "16/16, train_loss: 0.0575\n",
      "17/16, train_loss: 0.0735\n",
      "epoch 231 average loss: 0.0773\n",
      "----------\n",
      "epoch 232/600\n",
      "1/16, train_loss: 0.0705\n",
      "2/16, train_loss: 0.0676\n",
      "3/16, train_loss: 0.0777\n",
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      "16/16, train_loss: 0.0989\n",
      "17/16, train_loss: 0.0745\n",
      "epoch 232 average loss: 0.0791\n",
      "----------\n",
      "epoch 233/600\n",
      "1/16, train_loss: 0.1153\n",
      "2/16, train_loss: 0.0679\n",
      "3/16, train_loss: 0.0718\n",
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      "16/16, train_loss: 0.0716\n",
      "17/16, train_loss: 0.0601\n",
      "epoch 233 average loss: 0.0801\n",
      "----------\n",
      "epoch 234/600\n",
      "1/16, train_loss: 0.0781\n",
      "2/16, train_loss: 0.0723\n",
      "3/16, train_loss: 0.1390\n",
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      "16/16, train_loss: 0.0726\n",
      "17/16, train_loss: 0.0592\n",
      "epoch 234 average loss: 0.0842\n",
      "----------\n",
      "epoch 235/600\n",
      "1/16, train_loss: 0.0583\n",
      "2/16, train_loss: 0.0682\n",
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      "12/16, train_loss: 0.0864\n",
      "13/16, train_loss: 0.0981\n",
      "14/16, train_loss: 0.1095\n",
      "15/16, train_loss: 0.0913\n",
      "16/16, train_loss: 0.0726\n",
      "17/16, train_loss: 0.0725\n",
      "epoch 235 average loss: 0.0803\n",
      "saved new best metric model at the 235th epoch\n",
      "current epoch: 235 current mean dice: 0.8460 \n",
      "best mean dice: 0.8460  at epoch: 235\n",
      "----------\n",
      "epoch 236/600\n",
      "1/16, train_loss: 0.0637\n",
      "2/16, train_loss: 0.0474\n",
      "3/16, train_loss: 0.1535\n",
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      "6/16, train_loss: 0.0836\n",
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      "11/16, train_loss: 0.0963\n",
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      "15/16, train_loss: 0.0879\n",
      "16/16, train_loss: 0.0767\n",
      "17/16, train_loss: 0.0585\n",
      "epoch 236 average loss: 0.0805\n",
      "----------\n",
      "epoch 237/600\n",
      "1/16, train_loss: 0.0600\n",
      "2/16, train_loss: 0.0595\n",
      "3/16, train_loss: 0.1485\n",
      "4/16, train_loss: 0.0721\n",
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      "6/16, train_loss: 0.0834\n",
      "7/16, train_loss: 0.0793\n",
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      "16/16, train_loss: 0.1089\n",
      "17/16, train_loss: 0.0671\n",
      "epoch 237 average loss: 0.0815\n",
      "----------\n",
      "epoch 238/600\n",
      "1/16, train_loss: 0.0588\n",
      "2/16, train_loss: 0.0689\n",
      "3/16, train_loss: 0.0816\n",
      "4/16, train_loss: 0.1080\n",
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      "6/16, train_loss: 0.0853\n",
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      "9/16, train_loss: 0.0639\n",
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      "11/16, train_loss: 0.1214\n",
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      "15/16, train_loss: 0.3388\n",
      "16/16, train_loss: 0.0930\n",
      "17/16, train_loss: 0.0837\n",
      "epoch 238 average loss: 0.1029\n",
      "----------\n",
      "epoch 239/600\n",
      "1/16, train_loss: 0.0765\n",
      "2/16, train_loss: 0.0603\n",
      "3/16, train_loss: 0.2009\n",
      "4/16, train_loss: 0.0778\n",
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      "8/16, train_loss: 0.0906\n",
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      "10/16, train_loss: 0.1005\n",
      "11/16, train_loss: 0.1163\n",
      "12/16, train_loss: 0.0970\n",
      "13/16, train_loss: 0.0788\n",
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      "15/16, train_loss: 0.0746\n",
      "16/16, train_loss: 0.1022\n",
      "17/16, train_loss: 0.0728\n",
      "epoch 239 average loss: 0.0912\n",
      "----------\n",
      "epoch 240/600\n",
      "1/16, train_loss: 0.0664\n",
      "2/16, train_loss: 0.0562\n",
      "3/16, train_loss: 0.1103\n",
      "4/16, train_loss: 0.0790\n",
      "5/16, train_loss: 0.0693\n",
      "6/16, train_loss: 0.0900\n",
      "7/16, train_loss: 0.0756\n",
      "8/16, train_loss: 0.0898\n",
      "9/16, train_loss: 0.0664\n",
      "10/16, train_loss: 0.0903\n",
      "11/16, train_loss: 0.0715\n",
      "12/16, train_loss: 0.1198\n",
      "13/16, train_loss: 0.0833\n",
      "14/16, train_loss: 0.0645\n",
      "15/16, train_loss: 0.0849\n",
      "16/16, train_loss: 0.0782\n",
      "17/16, train_loss: 0.0628\n",
      "epoch 240 average loss: 0.0799\n",
      "current epoch: 240 current mean dice: 0.8322 \n",
      "best mean dice: 0.8460  at epoch: 235\n",
      "----------\n",
      "epoch 241/600\n",
      "1/16, train_loss: 0.0549\n",
      "2/16, train_loss: 0.0910\n",
      "3/16, train_loss: 0.0915\n",
      "4/16, train_loss: 0.0802\n",
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      "6/16, train_loss: 0.0845\n",
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      "8/16, train_loss: 0.0969\n",
      "9/16, train_loss: 0.0694\n",
      "10/16, train_loss: 0.0753\n",
      "11/16, train_loss: 0.0922\n",
      "12/16, train_loss: 0.1029\n",
      "13/16, train_loss: 0.0785\n",
      "14/16, train_loss: 0.0674\n",
      "15/16, train_loss: 0.0821\n",
      "16/16, train_loss: 0.0733\n",
      "17/16, train_loss: 0.0406\n",
      "epoch 241 average loss: 0.0787\n",
      "----------\n",
      "epoch 242/600\n",
      "1/16, train_loss: 0.0776\n",
      "2/16, train_loss: 0.0673\n",
      "3/16, train_loss: 0.0675\n",
      "4/16, train_loss: 0.0784\n",
      "5/16, train_loss: 0.0944\n",
      "6/16, train_loss: 0.0858\n",
      "7/16, train_loss: 0.1024\n",
      "8/16, train_loss: 0.0888\n",
      "9/16, train_loss: 0.0714\n",
      "10/16, train_loss: 0.1083\n",
      "11/16, train_loss: 0.0752\n",
      "12/16, train_loss: 0.1099\n",
      "13/16, train_loss: 0.1196\n",
      "14/16, train_loss: 0.0940\n",
      "15/16, train_loss: 0.0798\n",
      "16/16, train_loss: 0.0921\n",
      "17/16, train_loss: 0.0540\n",
      "epoch 242 average loss: 0.0863\n",
      "----------\n",
      "epoch 243/600\n",
      "1/16, train_loss: 0.0649\n",
      "2/16, train_loss: 0.0864\n",
      "3/16, train_loss: 0.0986\n",
      "4/16, train_loss: 0.0834\n",
      "5/16, train_loss: 0.0775\n",
      "6/16, train_loss: 0.0904\n",
      "7/16, train_loss: 0.0790\n",
      "8/16, train_loss: 0.0796\n",
      "9/16, train_loss: 0.0633\n",
      "10/16, train_loss: 0.0946\n",
      "11/16, train_loss: 0.0756\n",
      "12/16, train_loss: 0.1090\n",
      "13/16, train_loss: 0.0845\n",
      "14/16, train_loss: 0.0826\n",
      "15/16, train_loss: 0.1018\n",
      "16/16, train_loss: 0.0870\n",
      "17/16, train_loss: 0.0987\n",
      "epoch 243 average loss: 0.0857\n",
      "----------\n",
      "epoch 244/600\n",
      "1/16, train_loss: 0.0666\n",
      "2/16, train_loss: 0.0668\n",
      "3/16, train_loss: 0.0806\n",
      "4/16, train_loss: 0.0922\n",
      "5/16, train_loss: 0.1004\n",
      "6/16, train_loss: 0.0727\n",
      "7/16, train_loss: 0.0713\n",
      "8/16, train_loss: 0.0849\n",
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      "10/16, train_loss: 0.0822\n",
      "11/16, train_loss: 0.0826\n",
      "12/16, train_loss: 0.1752\n",
      "13/16, train_loss: 0.1032\n",
      "14/16, train_loss: 0.1167\n",
      "15/16, train_loss: 0.0729\n",
      "16/16, train_loss: 0.0929\n",
      "17/16, train_loss: 0.1284\n",
      "epoch 244 average loss: 0.0913\n",
      "----------\n",
      "epoch 245/600\n",
      "1/16, train_loss: 0.0668\n",
      "2/16, train_loss: 0.0592\n",
      "3/16, train_loss: 0.0932\n",
      "4/16, train_loss: 0.0853\n",
      "5/16, train_loss: 0.0829\n",
      "6/16, train_loss: 0.0959\n",
      "7/16, train_loss: 0.0969\n",
      "8/16, train_loss: 0.0895\n",
      "9/16, train_loss: 0.0826\n",
      "10/16, train_loss: 0.0837\n",
      "11/16, train_loss: 0.0951\n",
      "12/16, train_loss: 0.0917\n",
      "13/16, train_loss: 0.0786\n",
      "14/16, train_loss: 0.0824\n",
      "15/16, train_loss: 0.0772\n",
      "16/16, train_loss: 0.0939\n",
      "17/16, train_loss: 0.0916\n",
      "epoch 245 average loss: 0.0851\n",
      "saved new best metric model at the 245th epoch\n",
      "current epoch: 245 current mean dice: 0.8463 \n",
      "best mean dice: 0.8463  at epoch: 245\n",
      "----------\n",
      "epoch 246/600\n",
      "1/16, train_loss: 0.0732\n",
      "2/16, train_loss: 0.0644\n",
      "3/16, train_loss: 0.0666\n",
      "4/16, train_loss: 0.0703\n",
      "5/16, train_loss: 0.0724\n",
      "6/16, train_loss: 0.0815\n",
      "7/16, train_loss: 0.0880\n",
      "8/16, train_loss: 0.0832\n",
      "9/16, train_loss: 0.1201\n",
      "10/16, train_loss: 0.0779\n",
      "11/16, train_loss: 0.1158\n",
      "12/16, train_loss: 0.1089\n",
      "13/16, train_loss: 0.0778\n",
      "14/16, train_loss: 0.0992\n",
      "15/16, train_loss: 0.0721\n",
      "16/16, train_loss: 0.0709\n",
      "17/16, train_loss: 0.0596\n",
      "epoch 246 average loss: 0.0825\n",
      "----------\n",
      "epoch 247/600\n",
      "1/16, train_loss: 0.0789\n",
      "2/16, train_loss: 0.0752\n",
      "3/16, train_loss: 0.1193\n",
      "4/16, train_loss: 0.0730\n",
      "5/16, train_loss: 0.0884\n",
      "6/16, train_loss: 0.0767\n",
      "7/16, train_loss: 0.0802\n",
      "8/16, train_loss: 0.0906\n",
      "9/16, train_loss: 0.0697\n",
      "10/16, train_loss: 0.1229\n",
      "11/16, train_loss: 0.0832\n",
      "12/16, train_loss: 0.0863\n",
      "13/16, train_loss: 0.0768\n",
      "14/16, train_loss: 0.0849\n",
      "15/16, train_loss: 0.0687\n",
      "16/16, train_loss: 0.0748\n",
      "17/16, train_loss: 0.0855\n",
      "epoch 247 average loss: 0.0844\n",
      "----------\n",
      "epoch 248/600\n",
      "1/16, train_loss: 0.0620\n",
      "2/16, train_loss: 0.0753\n",
      "3/16, train_loss: 0.0745\n",
      "4/16, train_loss: 0.0830\n",
      "5/16, train_loss: 0.0815\n",
      "6/16, train_loss: 0.0886\n",
      "7/16, train_loss: 0.0737\n",
      "8/16, train_loss: 0.0724\n",
      "9/16, train_loss: 0.0763\n",
      "10/16, train_loss: 0.0906\n",
      "11/16, train_loss: 0.0772\n",
      "12/16, train_loss: 0.0909\n",
      "13/16, train_loss: 0.0726\n",
      "14/16, train_loss: 0.0628\n",
      "15/16, train_loss: 0.0912\n",
      "16/16, train_loss: 0.0965\n",
      "17/16, train_loss: 0.0585\n",
      "epoch 248 average loss: 0.0781\n",
      "----------\n",
      "epoch 249/600\n",
      "1/16, train_loss: 0.0783\n",
      "2/16, train_loss: 0.0543\n",
      "3/16, train_loss: 0.0682\n",
      "4/16, train_loss: 0.0859\n",
      "5/16, train_loss: 0.0640\n",
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      "8/16, train_loss: 0.0985\n",
      "9/16, train_loss: 0.0690\n",
      "10/16, train_loss: 0.1191\n",
      "11/16, train_loss: 0.0653\n",
      "12/16, train_loss: 0.1202\n",
      "13/16, train_loss: 0.0762\n",
      "14/16, train_loss: 0.0936\n",
      "15/16, train_loss: 0.0752\n",
      "16/16, train_loss: 0.0822\n",
      "17/16, train_loss: 0.0825\n",
      "epoch 249 average loss: 0.0822\n",
      "----------\n",
      "epoch 250/600\n",
      "1/16, train_loss: 0.0675\n",
      "2/16, train_loss: 0.0645\n",
      "3/16, train_loss: 0.0812\n",
      "4/16, train_loss: 0.0940\n",
      "5/16, train_loss: 0.0810\n",
      "6/16, train_loss: 0.0966\n",
      "7/16, train_loss: 0.0902\n",
      "8/16, train_loss: 0.1128\n",
      "9/16, train_loss: 0.0730\n",
      "10/16, train_loss: 0.0813\n",
      "11/16, train_loss: 0.0843\n",
      "12/16, train_loss: 0.1202\n",
      "13/16, train_loss: 0.1053\n",
      "14/16, train_loss: 0.1220\n",
      "15/16, train_loss: 0.0651\n",
      "16/16, train_loss: 0.0821\n",
      "17/16, train_loss: 0.1293\n",
      "epoch 250 average loss: 0.0912\n",
      "saved new best metric model at the 250th epoch\n",
      "current epoch: 250 current mean dice: 0.8534 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 251/600\n",
      "1/16, train_loss: 0.0645\n",
      "2/16, train_loss: 0.0678\n",
      "3/16, train_loss: 0.0966\n",
      "4/16, train_loss: 0.1043\n",
      "5/16, train_loss: 0.0813\n",
      "6/16, train_loss: 0.0960\n",
      "7/16, train_loss: 0.1036\n",
      "8/16, train_loss: 0.0984\n",
      "9/16, train_loss: 0.0656\n",
      "10/16, train_loss: 0.0660\n",
      "11/16, train_loss: 0.0718\n",
      "12/16, train_loss: 0.0993\n",
      "13/16, train_loss: 0.0703\n",
      "14/16, train_loss: 0.0758\n",
      "15/16, train_loss: 0.0751\n",
      "16/16, train_loss: 0.0740\n",
      "17/16, train_loss: 0.0825\n",
      "epoch 251 average loss: 0.0819\n",
      "----------\n",
      "epoch 252/600\n",
      "1/16, train_loss: 0.0969\n",
      "2/16, train_loss: 0.0653\n",
      "3/16, train_loss: 0.0800\n",
      "4/16, train_loss: 0.0980\n",
      "5/16, train_loss: 0.0615\n",
      "6/16, train_loss: 0.0856\n",
      "7/16, train_loss: 0.0793\n",
      "8/16, train_loss: 0.1115\n",
      "9/16, train_loss: 0.0565\n",
      "10/16, train_loss: 0.0858\n",
      "11/16, train_loss: 0.0888\n",
      "12/16, train_loss: 0.0811\n",
      "13/16, train_loss: 0.0819\n",
      "14/16, train_loss: 0.0694\n",
      "15/16, train_loss: 0.0729\n",
      "16/16, train_loss: 0.0703\n",
      "17/16, train_loss: 0.0903\n",
      "epoch 252 average loss: 0.0809\n",
      "----------\n",
      "epoch 253/600\n",
      "1/16, train_loss: 0.0661\n",
      "2/16, train_loss: 0.0762\n",
      "3/16, train_loss: 0.0733\n",
      "4/16, train_loss: 0.0694\n",
      "5/16, train_loss: 0.0705\n",
      "6/16, train_loss: 0.0787\n",
      "7/16, train_loss: 0.0665\n",
      "8/16, train_loss: 0.0775\n",
      "9/16, train_loss: 0.0785\n",
      "10/16, train_loss: 0.0804\n",
      "11/16, train_loss: 0.0806\n",
      "12/16, train_loss: 0.0745\n",
      "13/16, train_loss: 0.0761\n",
      "14/16, train_loss: 0.0833\n",
      "15/16, train_loss: 0.0691\n",
      "16/16, train_loss: 0.0660\n",
      "17/16, train_loss: 0.0912\n",
      "epoch 253 average loss: 0.0752\n",
      "----------\n",
      "epoch 254/600\n",
      "1/16, train_loss: 0.0522\n",
      "2/16, train_loss: 0.0602\n",
      "3/16, train_loss: 0.0753\n",
      "4/16, train_loss: 0.0671\n",
      "5/16, train_loss: 0.0718\n",
      "6/16, train_loss: 0.0853\n",
      "7/16, train_loss: 0.0744\n",
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      "10/16, train_loss: 0.0723\n",
      "11/16, train_loss: 0.1008\n",
      "12/16, train_loss: 0.1077\n",
      "13/16, train_loss: 0.0828\n",
      "14/16, train_loss: 0.0886\n",
      "15/16, train_loss: 0.0839\n",
      "16/16, train_loss: 0.0709\n",
      "17/16, train_loss: 0.0781\n",
      "epoch 254 average loss: 0.0769\n",
      "----------\n",
      "epoch 255/600\n",
      "1/16, train_loss: 0.0771\n",
      "2/16, train_loss: 0.0796\n",
      "3/16, train_loss: 0.0923\n",
      "4/16, train_loss: 0.0699\n",
      "5/16, train_loss: 0.0674\n",
      "6/16, train_loss: 0.0811\n",
      "7/16, train_loss: 0.0691\n",
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      "11/16, train_loss: 0.1071\n",
      "12/16, train_loss: 0.0841\n",
      "13/16, train_loss: 0.0688\n",
      "14/16, train_loss: 0.0578\n",
      "15/16, train_loss: 0.0641\n",
      "16/16, train_loss: 0.0991\n",
      "17/16, train_loss: 0.1083\n",
      "epoch 255 average loss: 0.0796\n",
      "current epoch: 255 current mean dice: 0.8410 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 256/600\n",
      "1/16, train_loss: 0.0839\n",
      "2/16, train_loss: 0.0623\n",
      "3/16, train_loss: 0.0955\n",
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      "10/16, train_loss: 0.0959\n",
      "11/16, train_loss: 0.0706\n",
      "12/16, train_loss: 0.1081\n",
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      "14/16, train_loss: 0.0700\n",
      "15/16, train_loss: 0.0652\n",
      "16/16, train_loss: 0.0823\n",
      "17/16, train_loss: 0.0505\n",
      "epoch 256 average loss: 0.0787\n",
      "----------\n",
      "epoch 257/600\n",
      "1/16, train_loss: 0.0552\n",
      "2/16, train_loss: 0.0574\n",
      "3/16, train_loss: 0.1003\n",
      "4/16, train_loss: 0.0682\n",
      "5/16, train_loss: 0.0793\n",
      "6/16, train_loss: 0.0886\n",
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      "10/16, train_loss: 0.0731\n",
      "11/16, train_loss: 0.0803\n",
      "12/16, train_loss: 0.0843\n",
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      "epoch 257 average loss: 0.0747\n",
      "----------\n",
      "epoch 258/600\n",
      "1/16, train_loss: 0.0587\n",
      "2/16, train_loss: 0.0689\n",
      "3/16, train_loss: 0.0805\n",
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      "epoch 258 average loss: 0.0762\n",
      "----------\n",
      "epoch 259/600\n",
      "1/16, train_loss: 0.0628\n",
      "2/16, train_loss: 0.0616\n",
      "3/16, train_loss: 0.1059\n",
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      "epoch 259 average loss: 0.0769\n",
      "----------\n",
      "epoch 260/600\n",
      "1/16, train_loss: 0.0606\n",
      "2/16, train_loss: 0.0616\n",
      "3/16, train_loss: 0.0907\n",
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      "16/16, train_loss: 0.0845\n",
      "17/16, train_loss: 0.0643\n",
      "epoch 260 average loss: 0.0782\n",
      "current epoch: 260 current mean dice: 0.8438 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 261/600\n",
      "1/16, train_loss: 0.0644\n",
      "2/16, train_loss: 0.0660\n",
      "3/16, train_loss: 0.1340\n",
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      "15/16, train_loss: 0.0926\n",
      "16/16, train_loss: 0.1005\n",
      "17/16, train_loss: 0.0534\n",
      "epoch 261 average loss: 0.0791\n",
      "----------\n",
      "epoch 262/600\n",
      "1/16, train_loss: 0.0666\n",
      "2/16, train_loss: 0.0604\n",
      "3/16, train_loss: 0.0659\n",
      "4/16, train_loss: 0.0804\n",
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      "11/16, train_loss: 0.0927\n",
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      "16/16, train_loss: 0.0620\n",
      "17/16, train_loss: 0.1078\n",
      "epoch 262 average loss: 0.0842\n",
      "----------\n",
      "epoch 263/600\n",
      "1/16, train_loss: 0.0627\n",
      "2/16, train_loss: 0.0842\n",
      "3/16, train_loss: 0.0852\n",
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      "15/16, train_loss: 0.0737\n",
      "16/16, train_loss: 0.0639\n",
      "17/16, train_loss: 0.0777\n",
      "epoch 263 average loss: 0.0833\n",
      "----------\n",
      "epoch 264/600\n",
      "1/16, train_loss: 0.0630\n",
      "2/16, train_loss: 0.0639\n",
      "3/16, train_loss: 0.1946\n",
      "4/16, train_loss: 0.0735\n",
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      "16/16, train_loss: 0.0934\n",
      "17/16, train_loss: 0.0723\n",
      "epoch 264 average loss: 0.0826\n",
      "----------\n",
      "epoch 265/600\n",
      "1/16, train_loss: 0.0541\n",
      "2/16, train_loss: 0.0590\n",
      "3/16, train_loss: 0.0889\n",
      "4/16, train_loss: 0.0716\n",
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      "14/16, train_loss: 0.0699\n",
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      "16/16, train_loss: 0.0918\n",
      "17/16, train_loss: 0.0607\n",
      "epoch 265 average loss: 0.0751\n",
      "current epoch: 265 current mean dice: 0.8354 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 266/600\n",
      "1/16, train_loss: 0.0573\n",
      "2/16, train_loss: 0.0634\n",
      "3/16, train_loss: 0.0792\n",
      "4/16, train_loss: 0.0778\n",
      "5/16, train_loss: 0.0998\n",
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      "11/16, train_loss: 0.0997\n",
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      "15/16, train_loss: 0.1109\n",
      "16/16, train_loss: 0.0849\n",
      "17/16, train_loss: 0.0904\n",
      "epoch 266 average loss: 0.0818\n",
      "----------\n",
      "epoch 267/600\n",
      "1/16, train_loss: 0.0742\n",
      "2/16, train_loss: 0.0467\n",
      "3/16, train_loss: 0.0980\n",
      "4/16, train_loss: 0.0656\n",
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      "11/16, train_loss: 0.0872\n",
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      "15/16, train_loss: 0.1844\n",
      "16/16, train_loss: 0.0643\n",
      "17/16, train_loss: 0.0687\n",
      "epoch 267 average loss: 0.0811\n",
      "----------\n",
      "epoch 268/600\n",
      "1/16, train_loss: 0.0753\n",
      "2/16, train_loss: 0.0710\n",
      "3/16, train_loss: 0.0690\n",
      "4/16, train_loss: 0.0758\n",
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      "12/16, train_loss: 0.1013\n",
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      "14/16, train_loss: 0.0739\n",
      "15/16, train_loss: 0.0643\n",
      "16/16, train_loss: 0.0817\n",
      "17/16, train_loss: 0.0447\n",
      "epoch 268 average loss: 0.0752\n",
      "----------\n",
      "epoch 269/600\n",
      "1/16, train_loss: 0.1230\n",
      "2/16, train_loss: 0.0707\n",
      "3/16, train_loss: 0.0721\n",
      "4/16, train_loss: 0.1293\n",
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      "15/16, train_loss: 0.1416\n",
      "16/16, train_loss: 0.0735\n",
      "17/16, train_loss: 0.0526\n",
      "epoch 269 average loss: 0.0880\n",
      "----------\n",
      "epoch 270/600\n",
      "1/16, train_loss: 0.0545\n",
      "2/16, train_loss: 0.0608\n",
      "3/16, train_loss: 0.0964\n",
      "4/16, train_loss: 0.0786\n",
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      "16/16, train_loss: 0.0864\n",
      "17/16, train_loss: 0.0699\n",
      "epoch 270 average loss: 0.0752\n",
      "current epoch: 270 current mean dice: 0.8327 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 271/600\n",
      "1/16, train_loss: 0.0702\n",
      "2/16, train_loss: 0.0657\n",
      "3/16, train_loss: 0.0713\n",
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      "15/16, train_loss: 0.0678\n",
      "16/16, train_loss: 0.0827\n",
      "17/16, train_loss: 0.0497\n",
      "epoch 271 average loss: 0.0733\n",
      "----------\n",
      "epoch 272/600\n",
      "1/16, train_loss: 0.0907\n",
      "2/16, train_loss: 0.0693\n",
      "3/16, train_loss: 0.0669\n",
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      "15/16, train_loss: 0.0859\n",
      "16/16, train_loss: 0.0890\n",
      "17/16, train_loss: 0.0638\n",
      "epoch 272 average loss: 0.0772\n",
      "----------\n",
      "epoch 273/600\n",
      "1/16, train_loss: 0.0452\n",
      "2/16, train_loss: 0.0512\n",
      "3/16, train_loss: 0.0673\n",
      "4/16, train_loss: 0.0663\n",
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      "16/16, train_loss: 0.0909\n",
      "17/16, train_loss: 0.0569\n",
      "epoch 273 average loss: 0.0766\n",
      "----------\n",
      "epoch 274/600\n",
      "1/16, train_loss: 0.0736\n",
      "2/16, train_loss: 0.0669\n",
      "3/16, train_loss: 0.0805\n",
      "4/16, train_loss: 0.0671\n",
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      "15/16, train_loss: 0.0638\n",
      "16/16, train_loss: 0.0587\n",
      "17/16, train_loss: 0.0709\n",
      "epoch 274 average loss: 0.0727\n",
      "----------\n",
      "epoch 275/600\n",
      "1/16, train_loss: 0.0857\n",
      "2/16, train_loss: 0.0637\n",
      "3/16, train_loss: 0.0787\n",
      "4/16, train_loss: 0.0751\n",
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      "6/16, train_loss: 0.0889\n",
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      "15/16, train_loss: 0.0756\n",
      "16/16, train_loss: 0.0663\n",
      "17/16, train_loss: 0.0591\n",
      "epoch 275 average loss: 0.0739\n",
      "current epoch: 275 current mean dice: 0.8452 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 276/600\n",
      "1/16, train_loss: 0.1154\n",
      "2/16, train_loss: 0.0615\n",
      "3/16, train_loss: 0.1063\n",
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      "15/16, train_loss: 0.0689\n",
      "16/16, train_loss: 0.0805\n",
      "17/16, train_loss: 0.0544\n",
      "epoch 276 average loss: 0.0802\n",
      "----------\n",
      "epoch 277/600\n",
      "1/16, train_loss: 0.0696\n",
      "2/16, train_loss: 0.0519\n",
      "3/16, train_loss: 0.0732\n",
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      "16/16, train_loss: 0.0710\n",
      "17/16, train_loss: 0.0699\n",
      "epoch 277 average loss: 0.0691\n",
      "----------\n",
      "epoch 278/600\n",
      "1/16, train_loss: 0.0604\n",
      "2/16, train_loss: 0.0655\n",
      "3/16, train_loss: 0.0670\n",
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      "epoch 278 average loss: 0.0704\n",
      "----------\n",
      "epoch 279/600\n",
      "1/16, train_loss: 0.0726\n",
      "2/16, train_loss: 0.0498\n",
      "3/16, train_loss: 0.0696\n",
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      "17/16, train_loss: 0.0677\n",
      "epoch 279 average loss: 0.0706\n",
      "----------\n",
      "epoch 280/600\n",
      "1/16, train_loss: 0.0576\n",
      "2/16, train_loss: 0.0543\n",
      "3/16, train_loss: 0.0740\n",
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      "15/16, train_loss: 0.0884\n",
      "16/16, train_loss: 0.0703\n",
      "17/16, train_loss: 0.0710\n",
      "epoch 280 average loss: 0.0729\n",
      "current epoch: 280 current mean dice: 0.8084 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 281/600\n",
      "1/16, train_loss: 0.0503\n",
      "2/16, train_loss: 0.0667\n",
      "3/16, train_loss: 0.1048\n",
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      "17/16, train_loss: 0.1061\n",
      "epoch 281 average loss: 0.0855\n",
      "----------\n",
      "epoch 282/600\n",
      "1/16, train_loss: 0.0494\n",
      "2/16, train_loss: 0.0512\n",
      "3/16, train_loss: 0.0815\n",
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      "16/16, train_loss: 0.0908\n",
      "17/16, train_loss: 0.0600\n",
      "epoch 282 average loss: 0.0721\n",
      "----------\n",
      "epoch 283/600\n",
      "1/16, train_loss: 0.0671\n",
      "2/16, train_loss: 0.1177\n",
      "3/16, train_loss: 0.1603\n",
      "4/16, train_loss: 0.0877\n",
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      "15/16, train_loss: 0.0891\n",
      "16/16, train_loss: 0.0778\n",
      "17/16, train_loss: 0.0791\n",
      "epoch 283 average loss: 0.0858\n",
      "----------\n",
      "epoch 284/600\n",
      "1/16, train_loss: 0.0627\n",
      "2/16, train_loss: 0.0627\n",
      "3/16, train_loss: 0.0851\n",
      "4/16, train_loss: 0.0700\n",
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      "6/16, train_loss: 0.0664\n",
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      "9/16, train_loss: 0.0703\n",
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      "15/16, train_loss: 0.1622\n",
      "16/16, train_loss: 0.0764\n",
      "17/16, train_loss: 0.0818\n",
      "epoch 284 average loss: 0.0877\n",
      "----------\n",
      "epoch 285/600\n",
      "1/16, train_loss: 0.0677\n",
      "2/16, train_loss: 0.0567\n",
      "3/16, train_loss: 0.1640\n",
      "4/16, train_loss: 0.0646\n",
      "5/16, train_loss: 0.0585\n",
      "6/16, train_loss: 0.0844\n",
      "7/16, train_loss: 0.0673\n",
      "8/16, train_loss: 0.0885\n",
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      "10/16, train_loss: 0.0929\n",
      "11/16, train_loss: 0.0775\n",
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      "13/16, train_loss: 0.0872\n",
      "14/16, train_loss: 0.0893\n",
      "15/16, train_loss: 0.0740\n",
      "16/16, train_loss: 0.0813\n",
      "17/16, train_loss: 0.0876\n",
      "epoch 285 average loss: 0.0814\n",
      "current epoch: 285 current mean dice: 0.8451 \n",
      "best mean dice: 0.8534  at epoch: 250\n",
      "----------\n",
      "epoch 286/600\n",
      "1/16, train_loss: 0.0608\n",
      "2/16, train_loss: 0.0610\n",
      "3/16, train_loss: 0.0838\n",
      "4/16, train_loss: 0.0688\n",
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      "15/16, train_loss: 0.0669\n",
      "16/16, train_loss: 0.0671\n",
      "17/16, train_loss: 0.0532\n",
      "epoch 286 average loss: 0.0776\n",
      "----------\n",
      "epoch 287/600\n",
      "1/16, train_loss: 0.0613\n",
      "2/16, train_loss: 0.0625\n",
      "3/16, train_loss: 0.0755\n",
      "4/16, train_loss: 0.0733\n",
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      "11/16, train_loss: 0.1492\n",
      "12/16, train_loss: 0.1017\n",
      "13/16, train_loss: 0.0885\n",
      "14/16, train_loss: 0.0697\n",
      "15/16, train_loss: 0.0859\n",
      "16/16, train_loss: 0.0778\n",
      "17/16, train_loss: 0.0689\n",
      "epoch 287 average loss: 0.0810\n",
      "----------\n",
      "epoch 288/600\n",
      "1/16, train_loss: 0.0740\n",
      "2/16, train_loss: 0.0497\n",
      "3/16, train_loss: 0.2736\n",
      "4/16, train_loss: 0.0642\n",
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      "11/16, train_loss: 0.0897\n",
      "12/16, train_loss: 0.0932\n",
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      "14/16, train_loss: 0.0608\n",
      "15/16, train_loss: 0.0720\n",
      "16/16, train_loss: 0.0855\n",
      "17/16, train_loss: 0.0737\n",
      "epoch 288 average loss: 0.0865\n",
      "----------\n",
      "epoch 289/600\n",
      "1/16, train_loss: 0.0540\n",
      "2/16, train_loss: 0.0695\n",
      "3/16, train_loss: 0.0714\n",
      "4/16, train_loss: 0.0757\n",
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      "14/16, train_loss: 0.1031\n",
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      "16/16, train_loss: 0.1026\n",
      "17/16, train_loss: 0.0812\n",
      "epoch 289 average loss: 0.0856\n",
      "----------\n",
      "epoch 290/600\n",
      "1/16, train_loss: 0.0590\n",
      "2/16, train_loss: 0.0657\n",
      "3/16, train_loss: 0.0740\n",
      "4/16, train_loss: 0.0699\n",
      "5/16, train_loss: 0.0748\n",
      "6/16, train_loss: 0.1004\n",
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      "12/16, train_loss: 0.0744\n",
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      "14/16, train_loss: 0.0658\n",
      "15/16, train_loss: 0.0948\n",
      "16/16, train_loss: 0.0985\n",
      "17/16, train_loss: 0.0504\n",
      "epoch 290 average loss: 0.0776\n",
      "saved new best metric model at the 290th epoch\n",
      "current epoch: 290 current mean dice: 0.8554 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 291/600\n",
      "1/16, train_loss: 0.0519\n",
      "2/16, train_loss: 0.0770\n",
      "3/16, train_loss: 0.0913\n",
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      "11/16, train_loss: 0.0767\n",
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      "16/16, train_loss: 0.0701\n",
      "17/16, train_loss: 0.0452\n",
      "epoch 291 average loss: 0.0726\n",
      "----------\n",
      "epoch 292/600\n",
      "1/16, train_loss: 0.0594\n",
      "2/16, train_loss: 0.0613\n",
      "3/16, train_loss: 0.0815\n",
      "4/16, train_loss: 0.0735\n",
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      "12/16, train_loss: 0.1002\n",
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      "15/16, train_loss: 0.0798\n",
      "16/16, train_loss: 0.0691\n",
      "17/16, train_loss: 0.1132\n",
      "epoch 292 average loss: 0.0744\n",
      "----------\n",
      "epoch 293/600\n",
      "1/16, train_loss: 0.0705\n",
      "2/16, train_loss: 0.0645\n",
      "3/16, train_loss: 0.1188\n",
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      "15/16, train_loss: 0.0612\n",
      "16/16, train_loss: 0.0917\n",
      "17/16, train_loss: 0.0623\n",
      "epoch 293 average loss: 0.0767\n",
      "----------\n",
      "epoch 294/600\n",
      "1/16, train_loss: 0.0615\n",
      "2/16, train_loss: 0.0578\n",
      "3/16, train_loss: 0.0593\n",
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      "16/16, train_loss: 0.0827\n",
      "17/16, train_loss: 0.0668\n",
      "epoch 294 average loss: 0.0705\n",
      "----------\n",
      "epoch 295/600\n",
      "1/16, train_loss: 0.0538\n",
      "2/16, train_loss: 0.0572\n",
      "3/16, train_loss: 0.0720\n",
      "4/16, train_loss: 0.0807\n",
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      "11/16, train_loss: 0.0872\n",
      "12/16, train_loss: 0.0915\n",
      "13/16, train_loss: 0.0746\n",
      "14/16, train_loss: 0.0936\n",
      "15/16, train_loss: 0.0647\n",
      "16/16, train_loss: 0.0703\n",
      "17/16, train_loss: 0.0535\n",
      "epoch 295 average loss: 0.0707\n",
      "current epoch: 295 current mean dice: 0.8519 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 296/600\n",
      "1/16, train_loss: 0.0537\n",
      "2/16, train_loss: 0.0719\n",
      "3/16, train_loss: 0.0696\n",
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      "7/16, train_loss: 0.0894\n",
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      "14/16, train_loss: 0.0877\n",
      "15/16, train_loss: 0.0704\n",
      "16/16, train_loss: 0.0964\n",
      "17/16, train_loss: 0.0712\n",
      "epoch 296 average loss: 0.0724\n",
      "----------\n",
      "epoch 297/600\n",
      "1/16, train_loss: 0.0525\n",
      "2/16, train_loss: 0.0511\n",
      "3/16, train_loss: 0.1118\n",
      "4/16, train_loss: 0.0867\n",
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      "11/16, train_loss: 0.0668\n",
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      "14/16, train_loss: 0.0993\n",
      "15/16, train_loss: 0.0731\n",
      "16/16, train_loss: 0.0782\n",
      "17/16, train_loss: 0.0635\n",
      "epoch 297 average loss: 0.0744\n",
      "----------\n",
      "epoch 298/600\n",
      "1/16, train_loss: 0.0527\n",
      "2/16, train_loss: 0.0600\n",
      "3/16, train_loss: 0.0725\n",
      "4/16, train_loss: 0.0823\n",
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      "11/16, train_loss: 0.0697\n",
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      "14/16, train_loss: 0.0784\n",
      "15/16, train_loss: 0.1015\n",
      "16/16, train_loss: 0.0784\n",
      "17/16, train_loss: 0.0576\n",
      "epoch 298 average loss: 0.0731\n",
      "----------\n",
      "epoch 299/600\n",
      "1/16, train_loss: 0.0647\n",
      "2/16, train_loss: 0.0760\n",
      "3/16, train_loss: 0.1015\n",
      "4/16, train_loss: 0.0967\n",
      "5/16, train_loss: 0.0798\n",
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      "11/16, train_loss: 0.0755\n",
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      "14/16, train_loss: 0.0717\n",
      "15/16, train_loss: 0.0704\n",
      "16/16, train_loss: 0.0738\n",
      "17/16, train_loss: 0.0377\n",
      "epoch 299 average loss: 0.0746\n",
      "----------\n",
      "epoch 300/600\n",
      "1/16, train_loss: 0.0537\n",
      "2/16, train_loss: 0.0543\n",
      "3/16, train_loss: 0.1188\n",
      "4/16, train_loss: 0.0727\n",
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      "11/16, train_loss: 0.0835\n",
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      "16/16, train_loss: 0.0748\n",
      "17/16, train_loss: 0.1748\n",
      "epoch 300 average loss: 0.0802\n",
      "current epoch: 300 current mean dice: 0.8415 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 301/600\n",
      "1/16, train_loss: 0.0632\n",
      "2/16, train_loss: 0.0598\n",
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      "16/16, train_loss: 0.0739\n",
      "17/16, train_loss: 0.0770\n",
      "epoch 301 average loss: 0.0780\n",
      "----------\n",
      "epoch 302/600\n",
      "1/16, train_loss: 0.0502\n",
      "2/16, train_loss: 0.0497\n",
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      "epoch 302 average loss: 0.0748\n",
      "----------\n",
      "epoch 303/600\n",
      "1/16, train_loss: 0.0666\n",
      "2/16, train_loss: 0.0664\n",
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      "epoch 303 average loss: 0.0765\n",
      "----------\n",
      "epoch 304/600\n",
      "1/16, train_loss: 0.0639\n",
      "2/16, train_loss: 0.0626\n",
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      "epoch 304 average loss: 0.0718\n",
      "----------\n",
      "epoch 305/600\n",
      "1/16, train_loss: 0.0595\n",
      "2/16, train_loss: 0.0597\n",
      "3/16, train_loss: 0.1419\n",
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      "16/16, train_loss: 0.0880\n",
      "17/16, train_loss: 0.0614\n",
      "epoch 305 average loss: 0.0833\n",
      "current epoch: 305 current mean dice: 0.8445 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 306/600\n",
      "1/16, train_loss: 0.0642\n",
      "2/16, train_loss: 0.0614\n",
      "3/16, train_loss: 0.1260\n",
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      "epoch 306 average loss: 0.0835\n",
      "----------\n",
      "epoch 307/600\n",
      "1/16, train_loss: 0.0579\n",
      "2/16, train_loss: 0.0678\n",
      "3/16, train_loss: 0.0899\n",
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      "16/16, train_loss: 0.0723\n",
      "17/16, train_loss: 0.0789\n",
      "epoch 307 average loss: 0.0818\n",
      "----------\n",
      "epoch 308/600\n",
      "1/16, train_loss: 0.0621\n",
      "2/16, train_loss: 0.0495\n",
      "3/16, train_loss: 0.0659\n",
      "4/16, train_loss: 0.0692\n",
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      "16/16, train_loss: 0.0773\n",
      "17/16, train_loss: 0.1035\n",
      "epoch 308 average loss: 0.0720\n",
      "----------\n",
      "epoch 309/600\n",
      "1/16, train_loss: 0.0615\n",
      "2/16, train_loss: 0.0638\n",
      "3/16, train_loss: 0.0680\n",
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      "11/16, train_loss: 0.0798\n",
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      "16/16, train_loss: 0.0910\n",
      "17/16, train_loss: 0.0808\n",
      "epoch 309 average loss: 0.0783\n",
      "----------\n",
      "epoch 310/600\n",
      "1/16, train_loss: 0.0693\n",
      "2/16, train_loss: 0.0601\n",
      "3/16, train_loss: 0.0562\n",
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      "15/16, train_loss: 0.0674\n",
      "16/16, train_loss: 0.0894\n",
      "17/16, train_loss: 0.0805\n",
      "epoch 310 average loss: 0.0798\n",
      "current epoch: 310 current mean dice: 0.8516 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 311/600\n",
      "1/16, train_loss: 0.0725\n",
      "2/16, train_loss: 0.0649\n",
      "3/16, train_loss: 0.0796\n",
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      "16/16, train_loss: 0.0632\n",
      "17/16, train_loss: 0.0731\n",
      "epoch 311 average loss: 0.0741\n",
      "----------\n",
      "epoch 312/600\n",
      "1/16, train_loss: 0.0573\n",
      "2/16, train_loss: 0.0575\n",
      "3/16, train_loss: 0.0649\n",
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      "15/16, train_loss: 0.0792\n",
      "16/16, train_loss: 0.0722\n",
      "17/16, train_loss: 0.0887\n",
      "epoch 312 average loss: 0.0732\n",
      "----------\n",
      "epoch 313/600\n",
      "1/16, train_loss: 0.0654\n",
      "2/16, train_loss: 0.0637\n",
      "3/16, train_loss: 0.0960\n",
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      "14/16, train_loss: 0.1101\n",
      "15/16, train_loss: 0.1004\n",
      "16/16, train_loss: 0.1062\n",
      "17/16, train_loss: 0.0681\n",
      "epoch 313 average loss: 0.0796\n",
      "----------\n",
      "epoch 314/600\n",
      "1/16, train_loss: 0.0539\n",
      "2/16, train_loss: 0.0568\n",
      "3/16, train_loss: 0.0598\n",
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      "16/16, train_loss: 0.0758\n",
      "17/16, train_loss: 0.0699\n",
      "epoch 314 average loss: 0.0838\n",
      "----------\n",
      "epoch 315/600\n",
      "1/16, train_loss: 0.0783\n",
      "2/16, train_loss: 0.0548\n",
      "3/16, train_loss: 0.0762\n",
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      "16/16, train_loss: 0.0790\n",
      "17/16, train_loss: 0.0600\n",
      "epoch 315 average loss: 0.0772\n",
      "current epoch: 315 current mean dice: 0.8041 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 316/600\n",
      "1/16, train_loss: 0.0636\n",
      "2/16, train_loss: 0.0636\n",
      "3/16, train_loss: 0.0799\n",
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      "16/16, train_loss: 0.0669\n",
      "17/16, train_loss: 0.1003\n",
      "epoch 316 average loss: 0.0750\n",
      "----------\n",
      "epoch 317/600\n",
      "1/16, train_loss: 0.1209\n",
      "2/16, train_loss: 0.0550\n",
      "3/16, train_loss: 0.0637\n",
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      "16/16, train_loss: 0.0843\n",
      "17/16, train_loss: 0.0764\n",
      "epoch 317 average loss: 0.0779\n",
      "----------\n",
      "epoch 318/600\n",
      "1/16, train_loss: 0.0484\n",
      "2/16, train_loss: 0.0561\n",
      "3/16, train_loss: 0.0654\n",
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      "15/16, train_loss: 0.0728\n",
      "16/16, train_loss: 0.0776\n",
      "17/16, train_loss: 0.1314\n",
      "epoch 318 average loss: 0.0749\n",
      "----------\n",
      "epoch 319/600\n",
      "1/16, train_loss: 0.0549\n",
      "2/16, train_loss: 0.0613\n",
      "3/16, train_loss: 0.0479\n",
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      "16/16, train_loss: 0.0721\n",
      "17/16, train_loss: 0.0932\n",
      "epoch 319 average loss: 0.0711\n",
      "----------\n",
      "epoch 320/600\n",
      "1/16, train_loss: 0.0528\n",
      "2/16, train_loss: 0.0520\n",
      "3/16, train_loss: 0.0927\n",
      "4/16, train_loss: 0.0849\n",
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      "15/16, train_loss: 0.0708\n",
      "16/16, train_loss: 0.0669\n",
      "17/16, train_loss: 0.0566\n",
      "epoch 320 average loss: 0.0707\n",
      "current epoch: 320 current mean dice: 0.8451 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 321/600\n",
      "1/16, train_loss: 0.0496\n",
      "2/16, train_loss: 0.0544\n",
      "3/16, train_loss: 0.0598\n",
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      "15/16, train_loss: 0.0629\n",
      "16/16, train_loss: 0.0797\n",
      "17/16, train_loss: 0.0537\n",
      "epoch 321 average loss: 0.0723\n",
      "----------\n",
      "epoch 322/600\n",
      "1/16, train_loss: 0.0578\n",
      "2/16, train_loss: 0.0826\n",
      "3/16, train_loss: 0.0659\n",
      "4/16, train_loss: 0.0749\n",
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      "16/16, train_loss: 0.0838\n",
      "17/16, train_loss: 0.0656\n",
      "epoch 322 average loss: 0.0744\n",
      "----------\n",
      "epoch 323/600\n",
      "1/16, train_loss: 0.0446\n",
      "2/16, train_loss: 0.0535\n",
      "3/16, train_loss: 0.1117\n",
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      "15/16, train_loss: 0.0626\n",
      "16/16, train_loss: 0.0780\n",
      "17/16, train_loss: 0.0609\n",
      "epoch 323 average loss: 0.0722\n",
      "----------\n",
      "epoch 324/600\n",
      "1/16, train_loss: 0.0524\n",
      "2/16, train_loss: 0.0464\n",
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      "epoch 324 average loss: 0.0690\n",
      "----------\n",
      "epoch 325/600\n",
      "1/16, train_loss: 0.0700\n",
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      "17/16, train_loss: 0.0457\n",
      "epoch 325 average loss: 0.0727\n",
      "current epoch: 325 current mean dice: 0.8504 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 326/600\n",
      "1/16, train_loss: 0.0556\n",
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      "epoch 326 average loss: 0.0766\n",
      "----------\n",
      "epoch 327/600\n",
      "1/16, train_loss: 0.0464\n",
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      "epoch 327 average loss: 0.0710\n",
      "----------\n",
      "epoch 328/600\n",
      "1/16, train_loss: 0.0640\n",
      "2/16, train_loss: 0.0564\n",
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      "epoch 328 average loss: 0.0731\n",
      "----------\n",
      "epoch 329/600\n",
      "1/16, train_loss: 0.0592\n",
      "2/16, train_loss: 0.0522\n",
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      "epoch 329 average loss: 0.0763\n",
      "----------\n",
      "epoch 330/600\n",
      "1/16, train_loss: 0.0551\n",
      "2/16, train_loss: 0.0561\n",
      "3/16, train_loss: 0.0778\n",
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      "16/16, train_loss: 0.0642\n",
      "17/16, train_loss: 0.0559\n",
      "epoch 330 average loss: 0.0749\n",
      "current epoch: 330 current mean dice: 0.8439 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 331/600\n",
      "1/16, train_loss: 0.0565\n",
      "2/16, train_loss: 0.0542\n",
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      "16/16, train_loss: 0.0871\n",
      "17/16, train_loss: 0.0494\n",
      "epoch 331 average loss: 0.0684\n",
      "----------\n",
      "epoch 332/600\n",
      "1/16, train_loss: 0.0645\n",
      "2/16, train_loss: 0.0536\n",
      "3/16, train_loss: 0.0598\n",
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      "17/16, train_loss: 0.1117\n",
      "epoch 332 average loss: 0.0665\n",
      "----------\n",
      "epoch 333/600\n",
      "1/16, train_loss: 0.0507\n",
      "2/16, train_loss: 0.0465\n",
      "3/16, train_loss: 0.0618\n",
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      "17/16, train_loss: 0.0810\n",
      "epoch 333 average loss: 0.0725\n",
      "----------\n",
      "epoch 334/600\n",
      "1/16, train_loss: 0.0863\n",
      "2/16, train_loss: 0.0585\n",
      "3/16, train_loss: 0.0994\n",
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      "16/16, train_loss: 0.1130\n",
      "17/16, train_loss: 0.0614\n",
      "epoch 334 average loss: 0.0776\n",
      "----------\n",
      "epoch 335/600\n",
      "1/16, train_loss: 0.0595\n",
      "2/16, train_loss: 0.1048\n",
      "3/16, train_loss: 0.2106\n",
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      "16/16, train_loss: 0.1013\n",
      "17/16, train_loss: 0.0806\n",
      "epoch 335 average loss: 0.0872\n",
      "current epoch: 335 current mean dice: 0.8439 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 336/600\n",
      "1/16, train_loss: 0.0388\n",
      "2/16, train_loss: 0.0522\n",
      "3/16, train_loss: 0.1089\n",
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      "epoch 336 average loss: 0.0695\n",
      "----------\n",
      "epoch 337/600\n",
      "1/16, train_loss: 0.0569\n",
      "2/16, train_loss: 0.0517\n",
      "3/16, train_loss: 0.0678\n",
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      "15/16, train_loss: 0.1500\n",
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      "17/16, train_loss: 0.1474\n",
      "epoch 337 average loss: 0.0779\n",
      "----------\n",
      "epoch 338/600\n",
      "1/16, train_loss: 0.0632\n",
      "2/16, train_loss: 0.0520\n",
      "3/16, train_loss: 0.0881\n",
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      "epoch 338 average loss: 0.0785\n",
      "----------\n",
      "epoch 339/600\n",
      "1/16, train_loss: 0.0623\n",
      "2/16, train_loss: 0.0487\n",
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      "16/16, train_loss: 0.0971\n",
      "17/16, train_loss: 0.0797\n",
      "epoch 339 average loss: 0.0715\n",
      "----------\n",
      "epoch 340/600\n",
      "1/16, train_loss: 0.0483\n",
      "2/16, train_loss: 0.0844\n",
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      "16/16, train_loss: 0.0641\n",
      "17/16, train_loss: 0.0809\n",
      "epoch 340 average loss: 0.0720\n",
      "current epoch: 340 current mean dice: 0.8554 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 341/600\n",
      "1/16, train_loss: 0.0648\n",
      "2/16, train_loss: 0.0524\n",
      "3/16, train_loss: 0.0895\n",
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      "17/16, train_loss: 0.0500\n",
      "epoch 341 average loss: 0.0740\n",
      "----------\n",
      "epoch 342/600\n",
      "1/16, train_loss: 0.0550\n",
      "2/16, train_loss: 0.0474\n",
      "3/16, train_loss: 0.0686\n",
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      "16/16, train_loss: 0.0614\n",
      "17/16, train_loss: 0.0471\n",
      "epoch 342 average loss: 0.0651\n",
      "----------\n",
      "epoch 343/600\n",
      "1/16, train_loss: 0.0545\n",
      "2/16, train_loss: 0.0600\n",
      "3/16, train_loss: 0.0694\n",
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      "16/16, train_loss: 0.0692\n",
      "17/16, train_loss: 0.1045\n",
      "epoch 343 average loss: 0.0728\n",
      "----------\n",
      "epoch 344/600\n",
      "1/16, train_loss: 0.0813\n",
      "2/16, train_loss: 0.0598\n",
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      "17/16, train_loss: 0.0531\n",
      "epoch 344 average loss: 0.0810\n",
      "----------\n",
      "epoch 345/600\n",
      "1/16, train_loss: 0.0801\n",
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      "16/16, train_loss: 0.0889\n",
      "17/16, train_loss: 0.0491\n",
      "epoch 345 average loss: 0.0732\n",
      "current epoch: 345 current mean dice: 0.8295 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 346/600\n",
      "1/16, train_loss: 0.0552\n",
      "2/16, train_loss: 0.0548\n",
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      "epoch 346 average loss: 0.0733\n",
      "----------\n",
      "epoch 347/600\n",
      "1/16, train_loss: 0.0517\n",
      "2/16, train_loss: 0.0523\n",
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      "epoch 347 average loss: 0.0715\n",
      "----------\n",
      "epoch 348/600\n",
      "1/16, train_loss: 0.0612\n",
      "2/16, train_loss: 0.0573\n",
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      "epoch 348 average loss: 0.0742\n",
      "----------\n",
      "epoch 349/600\n",
      "1/16, train_loss: 0.0716\n",
      "2/16, train_loss: 0.0544\n",
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      "epoch 349 average loss: 0.0806\n",
      "----------\n",
      "epoch 350/600\n",
      "1/16, train_loss: 0.0553\n",
      "2/16, train_loss: 0.0521\n",
      "3/16, train_loss: 0.0671\n",
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      "16/16, train_loss: 0.0924\n",
      "17/16, train_loss: 0.0522\n",
      "epoch 350 average loss: 0.0722\n",
      "current epoch: 350 current mean dice: 0.8425 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 351/600\n",
      "1/16, train_loss: 0.0468\n",
      "2/16, train_loss: 0.0628\n",
      "3/16, train_loss: 0.0622\n",
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      "16/16, train_loss: 0.0886\n",
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      "epoch 351 average loss: 0.0727\n",
      "----------\n",
      "epoch 352/600\n",
      "1/16, train_loss: 0.0464\n",
      "2/16, train_loss: 0.0524\n",
      "3/16, train_loss: 0.0602\n",
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      "16/16, train_loss: 0.0829\n",
      "17/16, train_loss: 0.0683\n",
      "epoch 352 average loss: 0.0673\n",
      "----------\n",
      "epoch 353/600\n",
      "1/16, train_loss: 0.0495\n",
      "2/16, train_loss: 0.0486\n",
      "3/16, train_loss: 0.0941\n",
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      "16/16, train_loss: 0.0707\n",
      "17/16, train_loss: 0.1202\n",
      "epoch 353 average loss: 0.0733\n",
      "----------\n",
      "epoch 354/600\n",
      "1/16, train_loss: 0.0432\n",
      "2/16, train_loss: 0.0557\n",
      "3/16, train_loss: 0.0573\n",
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      "16/16, train_loss: 0.0639\n",
      "17/16, train_loss: 0.0448\n",
      "epoch 354 average loss: 0.0642\n",
      "----------\n",
      "epoch 355/600\n",
      "1/16, train_loss: 0.0566\n",
      "2/16, train_loss: 0.0487\n",
      "3/16, train_loss: 0.0902\n",
      "4/16, train_loss: 0.0623\n",
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      "15/16, train_loss: 0.0628\n",
      "16/16, train_loss: 0.0717\n",
      "17/16, train_loss: 0.0713\n",
      "epoch 355 average loss: 0.0679\n",
      "current epoch: 355 current mean dice: 0.8492 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 356/600\n",
      "1/16, train_loss: 0.0591\n",
      "2/16, train_loss: 0.0611\n",
      "3/16, train_loss: 0.0745\n",
      "4/16, train_loss: 0.0694\n",
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      "16/16, train_loss: 0.0799\n",
      "17/16, train_loss: 0.0469\n",
      "epoch 356 average loss: 0.0673\n",
      "----------\n",
      "epoch 357/600\n",
      "1/16, train_loss: 0.0408\n",
      "2/16, train_loss: 0.0556\n",
      "3/16, train_loss: 0.0659\n",
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      "15/16, train_loss: 0.0743\n",
      "16/16, train_loss: 0.0616\n",
      "17/16, train_loss: 0.0416\n",
      "epoch 357 average loss: 0.0622\n",
      "----------\n",
      "epoch 358/600\n",
      "1/16, train_loss: 0.0461\n",
      "2/16, train_loss: 0.0422\n",
      "3/16, train_loss: 0.0626\n",
      "4/16, train_loss: 0.0796\n",
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      "15/16, train_loss: 0.0613\n",
      "16/16, train_loss: 0.0678\n",
      "17/16, train_loss: 0.0990\n",
      "epoch 358 average loss: 0.0697\n",
      "----------\n",
      "epoch 359/600\n",
      "1/16, train_loss: 0.0568\n",
      "2/16, train_loss: 0.0494\n",
      "3/16, train_loss: 0.0945\n",
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      "16/16, train_loss: 0.0629\n",
      "17/16, train_loss: 0.0483\n",
      "epoch 359 average loss: 0.0691\n",
      "----------\n",
      "epoch 360/600\n",
      "1/16, train_loss: 0.0661\n",
      "2/16, train_loss: 0.0563\n",
      "3/16, train_loss: 0.1128\n",
      "4/16, train_loss: 0.0671\n",
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      "15/16, train_loss: 0.0653\n",
      "16/16, train_loss: 0.0647\n",
      "17/16, train_loss: 0.0555\n",
      "epoch 360 average loss: 0.0727\n",
      "current epoch: 360 current mean dice: 0.8410 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 361/600\n",
      "1/16, train_loss: 0.0465\n",
      "2/16, train_loss: 0.0470\n",
      "3/16, train_loss: 0.0595\n",
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      "16/16, train_loss: 0.0744\n",
      "17/16, train_loss: 0.0685\n",
      "epoch 361 average loss: 0.0672\n",
      "----------\n",
      "epoch 362/600\n",
      "1/16, train_loss: 0.0667\n",
      "2/16, train_loss: 0.0478\n",
      "3/16, train_loss: 0.0916\n",
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      "15/16, train_loss: 0.0649\n",
      "16/16, train_loss: 0.0665\n",
      "17/16, train_loss: 0.0538\n",
      "epoch 362 average loss: 0.0671\n",
      "----------\n",
      "epoch 363/600\n",
      "1/16, train_loss: 0.0519\n",
      "2/16, train_loss: 0.0499\n",
      "3/16, train_loss: 0.0752\n",
      "4/16, train_loss: 0.0656\n",
      "5/16, train_loss: 0.0553\n",
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      "15/16, train_loss: 0.0594\n",
      "16/16, train_loss: 0.0798\n",
      "17/16, train_loss: 0.1062\n",
      "epoch 363 average loss: 0.0686\n",
      "----------\n",
      "epoch 364/600\n",
      "1/16, train_loss: 0.0627\n",
      "2/16, train_loss: 0.0502\n",
      "3/16, train_loss: 0.0857\n",
      "4/16, train_loss: 0.0689\n",
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      "14/16, train_loss: 0.0731\n",
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      "16/16, train_loss: 0.0943\n",
      "17/16, train_loss: 0.0387\n",
      "epoch 364 average loss: 0.0704\n",
      "----------\n",
      "epoch 365/600\n",
      "1/16, train_loss: 0.0594\n",
      "2/16, train_loss: 0.0562\n",
      "3/16, train_loss: 0.0686\n",
      "4/16, train_loss: 0.0879\n",
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      "16/16, train_loss: 0.0671\n",
      "17/16, train_loss: 0.0760\n",
      "epoch 365 average loss: 0.0716\n",
      "current epoch: 365 current mean dice: 0.8531 \n",
      "best mean dice: 0.8554  at epoch: 290\n",
      "----------\n",
      "epoch 366/600\n",
      "1/16, train_loss: 0.0558\n",
      "2/16, train_loss: 0.0536\n",
      "3/16, train_loss: 0.0719\n",
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      "16/16, train_loss: 0.0674\n",
      "17/16, train_loss: 0.0344\n",
      "epoch 366 average loss: 0.0640\n",
      "----------\n",
      "epoch 367/600\n",
      "1/16, train_loss: 0.0498\n",
      "2/16, train_loss: 0.0680\n",
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      "16/16, train_loss: 0.0735\n",
      "17/16, train_loss: 0.0536\n",
      "epoch 367 average loss: 0.0695\n",
      "----------\n",
      "epoch 368/600\n",
      "1/16, train_loss: 0.0724\n",
      "2/16, train_loss: 0.0642\n",
      "3/16, train_loss: 0.0711\n",
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      "8/16, train_loss: 0.0537\n",
      "9/16, train_loss: 0.0591\n",
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      "12/16, train_loss: 0.0767\n",
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      "15/16, train_loss: 0.0586\n",
      "16/16, train_loss: 0.0756\n",
      "17/16, train_loss: 0.0901\n",
      "epoch 368 average loss: 0.0706\n",
      "----------\n",
      "epoch 369/600\n",
      "1/16, train_loss: 0.0513\n",
      "2/16, train_loss: 0.0442\n",
      "3/16, train_loss: 0.0804\n",
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      "16/16, train_loss: 0.0752\n",
      "17/16, train_loss: 0.0937\n",
      "epoch 369 average loss: 0.0732\n",
      "----------\n",
      "epoch 370/600\n",
      "1/16, train_loss: 0.0546\n",
      "2/16, train_loss: 0.0677\n",
      "3/16, train_loss: 0.0500\n",
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      "11/16, train_loss: 0.0708\n",
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      "16/16, train_loss: 0.1087\n",
      "17/16, train_loss: 0.0481\n",
      "epoch 370 average loss: 0.0689\n",
      "saved new best metric model at the 370th epoch\n",
      "current epoch: 370 current mean dice: 0.8602 \n",
      "best mean dice: 0.8602  at epoch: 370\n",
      "----------\n",
      "epoch 371/600\n",
      "1/16, train_loss: 0.0514\n",
      "2/16, train_loss: 0.0514\n",
      "3/16, train_loss: 0.0724\n",
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      "17/16, train_loss: 0.0669\n",
      "epoch 371 average loss: 0.0662\n",
      "----------\n",
      "epoch 372/600\n",
      "1/16, train_loss: 0.0624\n",
      "2/16, train_loss: 0.0859\n",
      "3/16, train_loss: 0.0533\n",
      "4/16, train_loss: 0.0720\n",
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      "11/16, train_loss: 0.0798\n",
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      "16/16, train_loss: 0.0723\n",
      "17/16, train_loss: 0.0742\n",
      "epoch 372 average loss: 0.0716\n",
      "----------\n",
      "epoch 373/600\n",
      "1/16, train_loss: 0.0615\n",
      "2/16, train_loss: 0.0649\n",
      "3/16, train_loss: 0.0645\n",
      "4/16, train_loss: 0.0764\n",
      "5/16, train_loss: 0.0673\n",
      "6/16, train_loss: 0.0804\n",
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      "8/16, train_loss: 0.0659\n",
      "9/16, train_loss: 0.0570\n",
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      "11/16, train_loss: 0.0747\n",
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      "14/16, train_loss: 0.0696\n",
      "15/16, train_loss: 0.0628\n",
      "16/16, train_loss: 0.0553\n",
      "17/16, train_loss: 0.1036\n",
      "epoch 373 average loss: 0.0705\n",
      "----------\n",
      "epoch 374/600\n",
      "1/16, train_loss: 0.0532\n",
      "2/16, train_loss: 0.0549\n",
      "3/16, train_loss: 0.0840\n",
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      "8/16, train_loss: 0.0706\n",
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      "16/16, train_loss: 0.0920\n",
      "17/16, train_loss: 0.0921\n",
      "epoch 374 average loss: 0.0706\n",
      "----------\n",
      "epoch 375/600\n",
      "1/16, train_loss: 0.0567\n",
      "2/16, train_loss: 0.0457\n",
      "3/16, train_loss: 0.0868\n",
      "4/16, train_loss: 0.0687\n",
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      "9/16, train_loss: 0.0587\n",
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      "11/16, train_loss: 0.0689\n",
      "12/16, train_loss: 0.0864\n",
      "13/16, train_loss: 0.0777\n",
      "14/16, train_loss: 0.0656\n",
      "15/16, train_loss: 0.0584\n",
      "16/16, train_loss: 0.0722\n",
      "17/16, train_loss: 0.0363\n",
      "epoch 375 average loss: 0.0690\n",
      "current epoch: 375 current mean dice: 0.8467 \n",
      "best mean dice: 0.8602  at epoch: 370\n",
      "----------\n",
      "epoch 376/600\n",
      "1/16, train_loss: 0.0505\n",
      "2/16, train_loss: 0.0577\n",
      "3/16, train_loss: 0.0827\n",
      "4/16, train_loss: 0.0631\n",
      "5/16, train_loss: 0.0796\n",
      "6/16, train_loss: 0.0851\n",
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      "8/16, train_loss: 0.0613\n",
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      "11/16, train_loss: 0.0797\n",
      "12/16, train_loss: 0.0989\n",
      "13/16, train_loss: 0.0733\n",
      "14/16, train_loss: 0.0655\n",
      "15/16, train_loss: 0.0642\n",
      "16/16, train_loss: 0.0733\n",
      "17/16, train_loss: 0.0595\n",
      "epoch 376 average loss: 0.0695\n",
      "----------\n",
      "epoch 377/600\n",
      "1/16, train_loss: 0.0719\n",
      "2/16, train_loss: 0.0553\n",
      "3/16, train_loss: 0.0759\n",
      "4/16, train_loss: 0.0557\n",
      "5/16, train_loss: 0.0531\n",
      "6/16, train_loss: 0.0649\n",
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      "11/16, train_loss: 0.1245\n",
      "12/16, train_loss: 0.0738\n",
      "13/16, train_loss: 0.0594\n",
      "14/16, train_loss: 0.0529\n",
      "15/16, train_loss: 0.0720\n",
      "16/16, train_loss: 0.0680\n",
      "17/16, train_loss: 0.0496\n",
      "epoch 377 average loss: 0.0691\n",
      "----------\n",
      "epoch 378/600\n",
      "1/16, train_loss: 0.0546\n",
      "2/16, train_loss: 0.0726\n",
      "3/16, train_loss: 0.0647\n",
      "4/16, train_loss: 0.0758\n",
      "5/16, train_loss: 0.0669\n",
      "6/16, train_loss: 0.0712\n",
      "7/16, train_loss: 0.0584\n",
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      "10/16, train_loss: 0.0733\n",
      "11/16, train_loss: 0.1090\n",
      "12/16, train_loss: 0.0649\n",
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      "14/16, train_loss: 0.0775\n",
      "15/16, train_loss: 0.0734\n",
      "16/16, train_loss: 0.0651\n",
      "17/16, train_loss: 0.0802\n",
      "epoch 378 average loss: 0.0713\n",
      "----------\n",
      "epoch 379/600\n",
      "1/16, train_loss: 0.0579\n",
      "2/16, train_loss: 0.0533\n",
      "3/16, train_loss: 0.0724\n",
      "4/16, train_loss: 0.0672\n",
      "5/16, train_loss: 0.0573\n",
      "6/16, train_loss: 0.0768\n",
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      "11/16, train_loss: 0.0884\n",
      "12/16, train_loss: 0.1044\n",
      "13/16, train_loss: 0.0669\n",
      "14/16, train_loss: 0.0901\n",
      "15/16, train_loss: 0.0661\n",
      "16/16, train_loss: 0.0589\n",
      "17/16, train_loss: 0.0650\n",
      "epoch 379 average loss: 0.0687\n",
      "----------\n",
      "epoch 380/600\n",
      "1/16, train_loss: 0.0613\n",
      "2/16, train_loss: 0.0479\n",
      "3/16, train_loss: 0.0777\n",
      "4/16, train_loss: 0.0637\n",
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      "6/16, train_loss: 0.0666\n",
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      "11/16, train_loss: 0.0596\n",
      "12/16, train_loss: 0.0962\n",
      "13/16, train_loss: 0.0591\n",
      "14/16, train_loss: 0.1149\n",
      "15/16, train_loss: 0.0646\n",
      "16/16, train_loss: 0.0641\n",
      "17/16, train_loss: 0.0544\n",
      "epoch 380 average loss: 0.0670\n",
      "current epoch: 380 current mean dice: 0.8397 \n",
      "best mean dice: 0.8602  at epoch: 370\n",
      "----------\n",
      "epoch 381/600\n",
      "1/16, train_loss: 0.0529\n",
      "2/16, train_loss: 0.0511\n",
      "3/16, train_loss: 0.0638\n",
      "4/16, train_loss: 0.0636\n",
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      "11/16, train_loss: 0.0730\n",
      "12/16, train_loss: 0.0756\n",
      "13/16, train_loss: 0.0536\n",
      "14/16, train_loss: 0.0537\n",
      "15/16, train_loss: 0.0672\n",
      "16/16, train_loss: 0.0657\n",
      "17/16, train_loss: 0.0556\n",
      "epoch 381 average loss: 0.0631\n",
      "----------\n",
      "epoch 382/600\n",
      "1/16, train_loss: 0.0723\n",
      "2/16, train_loss: 0.0519\n",
      "3/16, train_loss: 0.0616\n",
      "4/16, train_loss: 0.0664\n",
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      "6/16, train_loss: 0.0673\n",
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      "11/16, train_loss: 0.0590\n",
      "12/16, train_loss: 0.0649\n",
      "13/16, train_loss: 0.0712\n",
      "14/16, train_loss: 0.0792\n",
      "15/16, train_loss: 0.0569\n",
      "16/16, train_loss: 0.0771\n",
      "17/16, train_loss: 0.0558\n",
      "epoch 382 average loss: 0.0641\n",
      "----------\n",
      "epoch 383/600\n",
      "1/16, train_loss: 0.0525\n",
      "2/16, train_loss: 0.0642\n",
      "3/16, train_loss: 0.1366\n",
      "4/16, train_loss: 0.0613\n",
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      "6/16, train_loss: 0.0634\n",
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      "11/16, train_loss: 0.0816\n",
      "12/16, train_loss: 0.0849\n",
      "13/16, train_loss: 0.0662\n",
      "14/16, train_loss: 0.0625\n",
      "15/16, train_loss: 0.0601\n",
      "16/16, train_loss: 0.0693\n",
      "17/16, train_loss: 0.0652\n",
      "epoch 383 average loss: 0.0722\n",
      "----------\n",
      "epoch 384/600\n",
      "1/16, train_loss: 0.0579\n",
      "2/16, train_loss: 0.0489\n",
      "3/16, train_loss: 0.0793\n",
      "4/16, train_loss: 0.0852\n",
      "5/16, train_loss: 0.0569\n",
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      "11/16, train_loss: 0.0640\n",
      "12/16, train_loss: 0.0841\n",
      "13/16, train_loss: 0.0791\n",
      "14/16, train_loss: 0.0690\n",
      "15/16, train_loss: 0.0805\n",
      "16/16, train_loss: 0.0723\n",
      "17/16, train_loss: 0.0689\n",
      "epoch 384 average loss: 0.0722\n",
      "----------\n",
      "epoch 385/600\n",
      "1/16, train_loss: 0.0516\n",
      "2/16, train_loss: 0.0875\n",
      "3/16, train_loss: 0.2439\n",
      "4/16, train_loss: 0.0614\n",
      "5/16, train_loss: 0.0652\n",
      "6/16, train_loss: 0.0597\n",
      "7/16, train_loss: 0.0613\n",
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      "9/16, train_loss: 0.0655\n",
      "10/16, train_loss: 0.0924\n",
      "11/16, train_loss: 0.0685\n",
      "12/16, train_loss: 0.0812\n",
      "13/16, train_loss: 0.0696\n",
      "14/16, train_loss: 0.0836\n",
      "15/16, train_loss: 0.0771\n",
      "16/16, train_loss: 0.0699\n",
      "17/16, train_loss: 0.0630\n",
      "epoch 385 average loss: 0.0801\n",
      "current epoch: 385 current mean dice: 0.8468 \n",
      "best mean dice: 0.8602  at epoch: 370\n",
      "----------\n",
      "epoch 386/600\n",
      "1/16, train_loss: 0.0495\n",
      "2/16, train_loss: 0.0557\n",
      "3/16, train_loss: 0.0770\n",
      "4/16, train_loss: 0.0586\n",
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      "11/16, train_loss: 0.0755\n",
      "12/16, train_loss: 0.0630\n",
      "13/16, train_loss: 0.0792\n",
      "14/16, train_loss: 0.0671\n",
      "15/16, train_loss: 0.1750\n",
      "16/16, train_loss: 0.0769\n",
      "17/16, train_loss: 0.0580\n",
      "epoch 386 average loss: 0.0757\n",
      "----------\n",
      "epoch 387/600\n",
      "1/16, train_loss: 0.0561\n",
      "2/16, train_loss: 0.0583\n",
      "3/16, train_loss: 0.0690\n",
      "4/16, train_loss: 0.0597\n",
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      "13/16, train_loss: 0.0661\n",
      "14/16, train_loss: 0.0632\n",
      "15/16, train_loss: 0.0676\n",
      "16/16, train_loss: 0.0594\n",
      "17/16, train_loss: 0.0939\n",
      "epoch 387 average loss: 0.0697\n",
      "----------\n",
      "epoch 388/600\n",
      "1/16, train_loss: 0.0526\n",
      "2/16, train_loss: 0.0465\n",
      "3/16, train_loss: 0.0864\n",
      "4/16, train_loss: 0.0717\n",
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      "11/16, train_loss: 0.1247\n",
      "12/16, train_loss: 0.0992\n",
      "13/16, train_loss: 0.0673\n",
      "14/16, train_loss: 0.0801\n",
      "15/16, train_loss: 0.0581\n",
      "16/16, train_loss: 0.1032\n",
      "17/16, train_loss: 0.0611\n",
      "epoch 388 average loss: 0.0730\n",
      "----------\n",
      "epoch 389/600\n",
      "1/16, train_loss: 0.0553\n",
      "2/16, train_loss: 0.0498\n",
      "3/16, train_loss: 0.0728\n",
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      "16/16, train_loss: 0.0665\n",
      "17/16, train_loss: 0.0705\n",
      "epoch 389 average loss: 0.0670\n",
      "----------\n",
      "epoch 390/600\n",
      "1/16, train_loss: 0.0566\n",
      "2/16, train_loss: 0.0543\n",
      "3/16, train_loss: 0.1083\n",
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      "10/16, train_loss: 0.0737\n",
      "11/16, train_loss: 0.1282\n",
      "12/16, train_loss: 0.0701\n",
      "13/16, train_loss: 0.0734\n",
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      "15/16, train_loss: 0.0645\n",
      "16/16, train_loss: 0.0732\n",
      "17/16, train_loss: 0.0798\n",
      "epoch 390 average loss: 0.0735\n",
      "current epoch: 390 current mean dice: 0.8487 \n",
      "best mean dice: 0.8602  at epoch: 370\n",
      "----------\n",
      "epoch 391/600\n",
      "1/16, train_loss: 0.0609\n",
      "2/16, train_loss: 0.0513\n",
      "3/16, train_loss: 0.1045\n",
      "4/16, train_loss: 0.0649\n",
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      "6/16, train_loss: 0.0679\n",
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      "15/16, train_loss: 0.0615\n",
      "16/16, train_loss: 0.0602\n",
      "17/16, train_loss: 0.0694\n",
      "epoch 391 average loss: 0.0702\n",
      "----------\n",
      "epoch 392/600\n",
      "1/16, train_loss: 0.0531\n",
      "2/16, train_loss: 0.0563\n",
      "3/16, train_loss: 0.0733\n",
      "4/16, train_loss: 0.0719\n",
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      "6/16, train_loss: 0.0778\n",
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      "15/16, train_loss: 0.0649\n",
      "16/16, train_loss: 0.0609\n",
      "17/16, train_loss: 0.0566\n",
      "epoch 392 average loss: 0.0652\n",
      "----------\n",
      "epoch 393/600\n",
      "1/16, train_loss: 0.0458\n",
      "2/16, train_loss: 0.0636\n",
      "3/16, train_loss: 0.0997\n",
      "4/16, train_loss: 0.0640\n",
      "5/16, train_loss: 0.0686\n",
      "6/16, train_loss: 0.0710\n",
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      "11/16, train_loss: 0.0645\n",
      "12/16, train_loss: 0.0779\n",
      "13/16, train_loss: 0.0644\n",
      "14/16, train_loss: 0.0707\n",
      "15/16, train_loss: 0.0563\n",
      "16/16, train_loss: 0.0645\n",
      "17/16, train_loss: 0.0532\n",
      "epoch 393 average loss: 0.0668\n",
      "----------\n",
      "epoch 394/600\n",
      "1/16, train_loss: 0.0458\n",
      "2/16, train_loss: 0.0494\n",
      "3/16, train_loss: 0.1606\n",
      "4/16, train_loss: 0.0702\n",
      "5/16, train_loss: 0.0670\n",
      "6/16, train_loss: 0.0529\n",
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      "9/16, train_loss: 0.0671\n",
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      "11/16, train_loss: 0.0703\n",
      "12/16, train_loss: 0.0698\n",
      "13/16, train_loss: 0.0728\n",
      "14/16, train_loss: 0.0611\n",
      "15/16, train_loss: 0.0782\n",
      "16/16, train_loss: 0.0752\n",
      "17/16, train_loss: 0.0385\n",
      "epoch 394 average loss: 0.0737\n",
      "----------\n",
      "epoch 395/600\n",
      "1/16, train_loss: 0.0492\n",
      "2/16, train_loss: 0.0561\n",
      "3/16, train_loss: 0.0631\n",
      "4/16, train_loss: 0.0702\n",
      "5/16, train_loss: 0.1015\n",
      "6/16, train_loss: 0.0781\n",
      "7/16, train_loss: 0.0859\n",
      "8/16, train_loss: 0.0547\n",
      "9/16, train_loss: 0.0643\n",
      "10/16, train_loss: 0.0737\n",
      "11/16, train_loss: 0.0820\n",
      "12/16, train_loss: 0.1093\n",
      "13/16, train_loss: 0.0596\n",
      "14/16, train_loss: 0.0591\n",
      "15/16, train_loss: 0.0723\n",
      "16/16, train_loss: 0.0528\n",
      "17/16, train_loss: 0.0495\n",
      "epoch 395 average loss: 0.0695\n",
      "current epoch: 395 current mean dice: 0.8190 \n",
      "best mean dice: 0.8602  at epoch: 370\n",
      "----------\n",
      "epoch 396/600\n",
      "1/16, train_loss: 0.0505\n",
      "2/16, train_loss: 0.0629\n",
      "3/16, train_loss: 0.0700\n",
      "4/16, train_loss: 0.0646\n",
      "5/16, train_loss: 0.0574\n",
      "6/16, train_loss: 0.0657\n",
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      "8/16, train_loss: 0.0870\n",
      "9/16, train_loss: 0.0559\n",
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      "11/16, train_loss: 0.1018\n",
      "12/16, train_loss: 0.0681\n",
      "13/16, train_loss: 0.0631\n",
      "14/16, train_loss: 0.0778\n",
      "15/16, train_loss: 0.0614\n",
      "16/16, train_loss: 0.0672\n",
      "17/16, train_loss: 0.0757\n",
      "epoch 396 average loss: 0.0693\n",
      "----------\n",
      "epoch 397/600\n",
      "1/16, train_loss: 0.0748\n",
      "2/16, train_loss: 0.0617\n",
      "3/16, train_loss: 0.0760\n",
      "4/16, train_loss: 0.0558\n",
      "5/16, train_loss: 0.0641\n",
      "6/16, train_loss: 0.0833\n",
      "7/16, train_loss: 0.0559\n",
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      "9/16, train_loss: 0.0758\n",
      "10/16, train_loss: 0.0832\n",
      "11/16, train_loss: 0.0939\n",
      "12/16, train_loss: 0.0676\n",
      "13/16, train_loss: 0.0764\n",
      "14/16, train_loss: 0.0654\n",
      "15/16, train_loss: 0.0653\n",
      "16/16, train_loss: 0.0759\n",
      "17/16, train_loss: 0.0498\n",
      "epoch 397 average loss: 0.0712\n",
      "----------\n",
      "epoch 398/600\n",
      "1/16, train_loss: 0.0844\n",
      "2/16, train_loss: 0.0454\n",
      "3/16, train_loss: 0.1274\n",
      "4/16, train_loss: 0.0929\n",
      "5/16, train_loss: 0.0674\n",
      "6/16, train_loss: 0.0717\n",
      "7/16, train_loss: 0.0662\n",
      "8/16, train_loss: 0.0885\n",
      "9/16, train_loss: 0.0492\n",
      "10/16, train_loss: 0.0572\n",
      "11/16, train_loss: 0.0568\n",
      "12/16, train_loss: 0.0667\n",
      "13/16, train_loss: 0.0660\n",
      "14/16, train_loss: 0.0734\n",
      "15/16, train_loss: 0.0929\n",
      "16/16, train_loss: 0.0660\n",
      "17/16, train_loss: 0.0532\n",
      "epoch 398 average loss: 0.0721\n",
      "----------\n",
      "epoch 399/600\n",
      "1/16, train_loss: 0.0560\n",
      "2/16, train_loss: 0.0569\n",
      "3/16, train_loss: 0.0627\n",
      "4/16, train_loss: 0.0680\n",
      "5/16, train_loss: 0.0558\n",
      "6/16, train_loss: 0.0802\n",
      "7/16, train_loss: 0.0659\n",
      "8/16, train_loss: 0.1089\n",
      "9/16, train_loss: 0.0625\n",
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      "11/16, train_loss: 0.0651\n",
      "12/16, train_loss: 0.0801\n",
      "13/16, train_loss: 0.0777\n",
      "14/16, train_loss: 0.0566\n",
      "15/16, train_loss: 0.0746\n",
      "16/16, train_loss: 0.0785\n",
      "17/16, train_loss: 0.0578\n",
      "epoch 399 average loss: 0.0700\n",
      "----------\n",
      "epoch 400/600\n",
      "1/16, train_loss: 0.0555\n",
      "2/16, train_loss: 0.0767\n",
      "3/16, train_loss: 0.1140\n",
      "4/16, train_loss: 0.0588\n",
      "5/16, train_loss: 0.0564\n",
      "6/16, train_loss: 0.0772\n",
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      "9/16, train_loss: 0.0593\n",
      "10/16, train_loss: 0.0781\n",
      "11/16, train_loss: 0.0706\n",
      "12/16, train_loss: 0.0776\n",
      "13/16, train_loss: 0.0794\n",
      "14/16, train_loss: 0.0750\n",
      "15/16, train_loss: 0.0534\n",
      "16/16, train_loss: 0.0657\n",
      "17/16, train_loss: 0.0412\n",
      "epoch 400 average loss: 0.0683\n",
      "saved new best metric model at the 400th epoch\n",
      "current epoch: 400 current mean dice: 0.8646 \n",
      "best mean dice: 0.8646  at epoch: 400\n",
      "----------\n",
      "epoch 401/600\n",
      "1/16, train_loss: 0.0495\n",
      "2/16, train_loss: 0.0615\n",
      "3/16, train_loss: 0.1144\n",
      "4/16, train_loss: 0.0713\n",
      "5/16, train_loss: 0.0769\n",
      "6/16, train_loss: 0.0677\n",
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      "11/16, train_loss: 0.0867\n",
      "12/16, train_loss: 0.0764\n",
      "13/16, train_loss: 0.0779\n",
      "14/16, train_loss: 0.0557\n",
      "15/16, train_loss: 0.0567\n",
      "16/16, train_loss: 0.0747\n",
      "17/16, train_loss: 0.0440\n",
      "epoch 401 average loss: 0.0687\n",
      "----------\n",
      "epoch 402/600\n",
      "1/16, train_loss: 0.0688\n",
      "2/16, train_loss: 0.0570\n",
      "3/16, train_loss: 0.0747\n",
      "4/16, train_loss: 0.0689\n",
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      "6/16, train_loss: 0.0613\n",
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      "11/16, train_loss: 0.0590\n",
      "12/16, train_loss: 0.0655\n",
      "13/16, train_loss: 0.0636\n",
      "14/16, train_loss: 0.0817\n",
      "15/16, train_loss: 0.0750\n",
      "16/16, train_loss: 0.0774\n",
      "17/16, train_loss: 0.0566\n",
      "epoch 402 average loss: 0.0694\n",
      "----------\n",
      "epoch 403/600\n",
      "1/16, train_loss: 0.0682\n",
      "2/16, train_loss: 0.0683\n",
      "3/16, train_loss: 0.1289\n",
      "4/16, train_loss: 0.0650\n",
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      "6/16, train_loss: 0.0840\n",
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      "11/16, train_loss: 0.0595\n",
      "12/16, train_loss: 0.0844\n",
      "13/16, train_loss: 0.0713\n",
      "14/16, train_loss: 0.0920\n",
      "15/16, train_loss: 0.0741\n",
      "16/16, train_loss: 0.0663\n",
      "17/16, train_loss: 0.0477\n",
      "epoch 403 average loss: 0.0746\n",
      "----------\n",
      "epoch 404/600\n",
      "1/16, train_loss: 0.0634\n",
      "2/16, train_loss: 0.0572\n",
      "3/16, train_loss: 0.0672\n",
      "4/16, train_loss: 0.0571\n",
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      "11/16, train_loss: 0.0543\n",
      "12/16, train_loss: 0.0951\n",
      "13/16, train_loss: 0.0731\n",
      "14/16, train_loss: 0.0593\n",
      "15/16, train_loss: 0.0668\n",
      "16/16, train_loss: 0.0804\n",
      "17/16, train_loss: 0.0769\n",
      "epoch 404 average loss: 0.0704\n",
      "----------\n",
      "epoch 405/600\n",
      "1/16, train_loss: 0.0572\n",
      "2/16, train_loss: 0.0550\n",
      "3/16, train_loss: 0.0595\n",
      "4/16, train_loss: 0.0590\n",
      "5/16, train_loss: 0.0545\n",
      "6/16, train_loss: 0.0794\n",
      "7/16, train_loss: 0.0677\n",
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      "10/16, train_loss: 0.0671\n",
      "11/16, train_loss: 0.0746\n",
      "12/16, train_loss: 0.1030\n",
      "13/16, train_loss: 0.0830\n",
      "14/16, train_loss: 0.0738\n",
      "15/16, train_loss: 0.0665\n",
      "16/16, train_loss: 0.0761\n",
      "17/16, train_loss: 0.0721\n",
      "epoch 405 average loss: 0.0685\n",
      "current epoch: 405 current mean dice: 0.8635 \n",
      "best mean dice: 0.8646  at epoch: 400\n",
      "----------\n",
      "epoch 406/600\n",
      "1/16, train_loss: 0.0653\n",
      "2/16, train_loss: 0.0458\n",
      "3/16, train_loss: 0.0533\n",
      "4/16, train_loss: 0.0681\n",
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      "11/16, train_loss: 0.0801\n",
      "12/16, train_loss: 0.0728\n",
      "13/16, train_loss: 0.0672\n",
      "14/16, train_loss: 0.0686\n",
      "15/16, train_loss: 0.0724\n",
      "16/16, train_loss: 0.0613\n",
      "17/16, train_loss: 0.0687\n",
      "epoch 406 average loss: 0.0654\n",
      "----------\n",
      "epoch 407/600\n",
      "1/16, train_loss: 0.0559\n",
      "2/16, train_loss: 0.0427\n",
      "3/16, train_loss: 0.0531\n",
      "4/16, train_loss: 0.0659\n",
      "5/16, train_loss: 0.0645\n",
      "6/16, train_loss: 0.0689\n",
      "7/16, train_loss: 0.0779\n",
      "8/16, train_loss: 0.0668\n",
      "9/16, train_loss: 0.0605\n",
      "10/16, train_loss: 0.0749\n",
      "11/16, train_loss: 0.0643\n",
      "12/16, train_loss: 0.0551\n",
      "13/16, train_loss: 0.0662\n",
      "14/16, train_loss: 0.0594\n",
      "15/16, train_loss: 0.0702\n",
      "16/16, train_loss: 0.0693\n",
      "17/16, train_loss: 0.0525\n",
      "epoch 407 average loss: 0.0628\n",
      "----------\n",
      "epoch 408/600\n",
      "1/16, train_loss: 0.0500\n",
      "2/16, train_loss: 0.0636\n",
      "3/16, train_loss: 0.0530\n",
      "4/16, train_loss: 0.0505\n",
      "5/16, train_loss: 0.0574\n",
      "6/16, train_loss: 0.0716\n",
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      "11/16, train_loss: 0.1051\n",
      "12/16, train_loss: 0.0785\n",
      "13/16, train_loss: 0.0776\n",
      "14/16, train_loss: 0.0836\n",
      "15/16, train_loss: 0.0517\n",
      "16/16, train_loss: 0.0665\n",
      "17/16, train_loss: 0.0623\n",
      "epoch 408 average loss: 0.0655\n",
      "----------\n",
      "epoch 409/600\n",
      "1/16, train_loss: 0.0410\n",
      "2/16, train_loss: 0.0479\n",
      "3/16, train_loss: 0.1259\n",
      "4/16, train_loss: 0.0734\n",
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      "6/16, train_loss: 0.0634\n",
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      "9/16, train_loss: 0.0596\n",
      "10/16, train_loss: 0.0906\n",
      "11/16, train_loss: 0.0779\n",
      "12/16, train_loss: 0.0818\n",
      "13/16, train_loss: 0.0721\n",
      "14/16, train_loss: 0.0605\n",
      "15/16, train_loss: 0.0699\n",
      "16/16, train_loss: 0.0926\n",
      "17/16, train_loss: 0.0422\n",
      "epoch 409 average loss: 0.0702\n",
      "----------\n",
      "epoch 410/600\n",
      "1/16, train_loss: 0.0631\n",
      "2/16, train_loss: 0.0549\n",
      "3/16, train_loss: 0.0733\n",
      "4/16, train_loss: 0.0826\n",
      "5/16, train_loss: 0.0581\n",
      "6/16, train_loss: 0.0758\n",
      "7/16, train_loss: 0.0666\n",
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      "10/16, train_loss: 0.0621\n",
      "11/16, train_loss: 0.0742\n",
      "12/16, train_loss: 0.0631\n",
      "13/16, train_loss: 0.0754\n",
      "14/16, train_loss: 0.0686\n",
      "15/16, train_loss: 0.0618\n",
      "16/16, train_loss: 0.0709\n",
      "17/16, train_loss: 0.0897\n",
      "epoch 410 average loss: 0.0694\n",
      "current epoch: 410 current mean dice: 0.8475 \n",
      "best mean dice: 0.8646  at epoch: 400\n",
      "----------\n",
      "epoch 411/600\n",
      "1/16, train_loss: 0.0514\n",
      "2/16, train_loss: 0.0618\n",
      "3/16, train_loss: 0.0544\n",
      "4/16, train_loss: 0.0692\n",
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      "11/16, train_loss: 0.0958\n",
      "12/16, train_loss: 0.0809\n",
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      "14/16, train_loss: 0.0673\n",
      "15/16, train_loss: 0.0622\n",
      "16/16, train_loss: 0.0683\n",
      "17/16, train_loss: 0.0709\n",
      "epoch 411 average loss: 0.0679\n",
      "----------\n",
      "epoch 412/600\n",
      "1/16, train_loss: 0.0591\n",
      "2/16, train_loss: 0.0488\n",
      "3/16, train_loss: 0.0778\n",
      "4/16, train_loss: 0.1015\n",
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      "11/16, train_loss: 0.0692\n",
      "12/16, train_loss: 0.0605\n",
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      "epoch 412 average loss: 0.0646\n",
      "----------\n",
      "epoch 413/600\n",
      "1/16, train_loss: 0.0791\n",
      "2/16, train_loss: 0.0622\n",
      "3/16, train_loss: 0.0650\n",
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      "epoch 413 average loss: 0.0713\n",
      "----------\n",
      "epoch 414/600\n",
      "1/16, train_loss: 0.0547\n",
      "2/16, train_loss: 0.0519\n",
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      "epoch 414 average loss: 0.0647\n",
      "----------\n",
      "epoch 415/600\n",
      "1/16, train_loss: 0.0518\n",
      "2/16, train_loss: 0.0455\n",
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      "15/16, train_loss: 0.0677\n",
      "16/16, train_loss: 0.0649\n",
      "17/16, train_loss: 0.0554\n",
      "epoch 415 average loss: 0.0649\n",
      "saved new best metric model at the 415th epoch\n",
      "current epoch: 415 current mean dice: 0.8716 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 416/600\n",
      "1/16, train_loss: 0.0624\n",
      "2/16, train_loss: 0.0523\n",
      "3/16, train_loss: 0.0546\n",
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      "epoch 416 average loss: 0.0695\n",
      "----------\n",
      "epoch 417/600\n",
      "1/16, train_loss: 0.0607\n",
      "2/16, train_loss: 0.0549\n",
      "3/16, train_loss: 0.0695\n",
      "4/16, train_loss: 0.0695\n",
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      "16/16, train_loss: 0.0630\n",
      "17/16, train_loss: 0.0497\n",
      "epoch 417 average loss: 0.0651\n",
      "----------\n",
      "epoch 418/600\n",
      "1/16, train_loss: 0.0590\n",
      "2/16, train_loss: 0.0457\n",
      "3/16, train_loss: 0.1710\n",
      "4/16, train_loss: 0.0647\n",
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      "17/16, train_loss: 0.0658\n",
      "epoch 418 average loss: 0.0720\n",
      "----------\n",
      "epoch 419/600\n",
      "1/16, train_loss: 0.0577\n",
      "2/16, train_loss: 0.0586\n",
      "3/16, train_loss: 0.0756\n",
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      "15/16, train_loss: 0.0536\n",
      "16/16, train_loss: 0.0738\n",
      "17/16, train_loss: 0.0675\n",
      "epoch 419 average loss: 0.0671\n",
      "----------\n",
      "epoch 420/600\n",
      "1/16, train_loss: 0.0536\n",
      "2/16, train_loss: 0.0848\n",
      "3/16, train_loss: 0.0696\n",
      "4/16, train_loss: 0.0905\n",
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      "6/16, train_loss: 0.0701\n",
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      "11/16, train_loss: 0.0907\n",
      "12/16, train_loss: 0.0796\n",
      "13/16, train_loss: 0.0754\n",
      "14/16, train_loss: 0.0764\n",
      "15/16, train_loss: 0.0880\n",
      "16/16, train_loss: 0.0740\n",
      "17/16, train_loss: 0.0460\n",
      "epoch 420 average loss: 0.0753\n",
      "current epoch: 420 current mean dice: 0.8675 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 421/600\n",
      "1/16, train_loss: 0.0467\n",
      "2/16, train_loss: 0.0517\n",
      "3/16, train_loss: 0.0902\n",
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      "7/16, train_loss: 0.0879\n",
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      "11/16, train_loss: 0.0951\n",
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      "14/16, train_loss: 0.0681\n",
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      "16/16, train_loss: 0.0659\n",
      "17/16, train_loss: 0.0835\n",
      "epoch 421 average loss: 0.0701\n",
      "----------\n",
      "epoch 422/600\n",
      "1/16, train_loss: 0.0487\n",
      "2/16, train_loss: 0.0471\n",
      "3/16, train_loss: 0.0560\n",
      "4/16, train_loss: 0.0520\n",
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      "11/16, train_loss: 0.0704\n",
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      "14/16, train_loss: 0.0715\n",
      "15/16, train_loss: 0.0584\n",
      "16/16, train_loss: 0.0658\n",
      "17/16, train_loss: 0.0398\n",
      "epoch 422 average loss: 0.0598\n",
      "----------\n",
      "epoch 423/600\n",
      "1/16, train_loss: 0.0496\n",
      "2/16, train_loss: 0.0547\n",
      "3/16, train_loss: 0.0565\n",
      "4/16, train_loss: 0.0584\n",
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      "11/16, train_loss: 0.0670\n",
      "12/16, train_loss: 0.0694\n",
      "13/16, train_loss: 0.0876\n",
      "14/16, train_loss: 0.0607\n",
      "15/16, train_loss: 0.0624\n",
      "16/16, train_loss: 0.0905\n",
      "17/16, train_loss: 0.0933\n",
      "epoch 423 average loss: 0.0676\n",
      "----------\n",
      "epoch 424/600\n",
      "1/16, train_loss: 0.0470\n",
      "2/16, train_loss: 0.0511\n",
      "3/16, train_loss: 0.0732\n",
      "4/16, train_loss: 0.0650\n",
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      "11/16, train_loss: 0.0527\n",
      "12/16, train_loss: 0.0787\n",
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      "14/16, train_loss: 0.0706\n",
      "15/16, train_loss: 0.0769\n",
      "16/16, train_loss: 0.0650\n",
      "17/16, train_loss: 0.0564\n",
      "epoch 424 average loss: 0.0640\n",
      "----------\n",
      "epoch 425/600\n",
      "1/16, train_loss: 0.0520\n",
      "2/16, train_loss: 0.0497\n",
      "3/16, train_loss: 0.0537\n",
      "4/16, train_loss: 0.0605\n",
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      "11/16, train_loss: 0.1009\n",
      "12/16, train_loss: 0.0766\n",
      "13/16, train_loss: 0.0709\n",
      "14/16, train_loss: 0.0655\n",
      "15/16, train_loss: 0.0707\n",
      "16/16, train_loss: 0.0681\n",
      "17/16, train_loss: 0.0468\n",
      "epoch 425 average loss: 0.0656\n",
      "current epoch: 425 current mean dice: 0.8561 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 426/600\n",
      "1/16, train_loss: 0.0491\n",
      "2/16, train_loss: 0.0488\n",
      "3/16, train_loss: 0.0718\n",
      "4/16, train_loss: 0.0636\n",
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      "14/16, train_loss: 0.0584\n",
      "15/16, train_loss: 0.0730\n",
      "16/16, train_loss: 0.0575\n",
      "17/16, train_loss: 0.0459\n",
      "epoch 426 average loss: 0.0620\n",
      "----------\n",
      "epoch 427/600\n",
      "1/16, train_loss: 0.0531\n",
      "2/16, train_loss: 0.0489\n",
      "3/16, train_loss: 0.0622\n",
      "4/16, train_loss: 0.0654\n",
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      "6/16, train_loss: 0.0531\n",
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      "11/16, train_loss: 0.1136\n",
      "12/16, train_loss: 0.0715\n",
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      "14/16, train_loss: 0.0790\n",
      "15/16, train_loss: 0.0582\n",
      "16/16, train_loss: 0.0664\n",
      "17/16, train_loss: 0.0717\n",
      "epoch 427 average loss: 0.0652\n",
      "----------\n",
      "epoch 428/600\n",
      "1/16, train_loss: 0.0566\n",
      "2/16, train_loss: 0.0504\n",
      "3/16, train_loss: 0.0729\n",
      "4/16, train_loss: 0.0633\n",
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      "11/16, train_loss: 0.0684\n",
      "12/16, train_loss: 0.0809\n",
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      "14/16, train_loss: 0.0646\n",
      "15/16, train_loss: 0.0588\n",
      "16/16, train_loss: 0.0554\n",
      "17/16, train_loss: 0.0576\n",
      "epoch 428 average loss: 0.0632\n",
      "----------\n",
      "epoch 429/600\n",
      "1/16, train_loss: 0.0590\n",
      "2/16, train_loss: 0.0500\n",
      "3/16, train_loss: 0.0687\n",
      "4/16, train_loss: 0.0602\n",
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      "14/16, train_loss: 0.0675\n",
      "15/16, train_loss: 0.0605\n",
      "16/16, train_loss: 0.0692\n",
      "17/16, train_loss: 0.0337\n",
      "epoch 429 average loss: 0.0606\n",
      "----------\n",
      "epoch 430/600\n",
      "1/16, train_loss: 0.0441\n",
      "2/16, train_loss: 0.0594\n",
      "3/16, train_loss: 0.0598\n",
      "4/16, train_loss: 0.0761\n",
      "5/16, train_loss: 0.0489\n",
      "6/16, train_loss: 0.0577\n",
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      "9/16, train_loss: 0.0616\n",
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      "11/16, train_loss: 0.0639\n",
      "12/16, train_loss: 0.0761\n",
      "13/16, train_loss: 0.0707\n",
      "14/16, train_loss: 0.0573\n",
      "15/16, train_loss: 0.0492\n",
      "16/16, train_loss: 0.0658\n",
      "17/16, train_loss: 0.0513\n",
      "epoch 430 average loss: 0.0606\n",
      "current epoch: 430 current mean dice: 0.8546 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 431/600\n",
      "1/16, train_loss: 0.0615\n",
      "2/16, train_loss: 0.0462\n",
      "3/16, train_loss: 0.0550\n",
      "4/16, train_loss: 0.0665\n",
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      "11/16, train_loss: 0.0793\n",
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      "15/16, train_loss: 0.0613\n",
      "16/16, train_loss: 0.0629\n",
      "17/16, train_loss: 0.0461\n",
      "epoch 431 average loss: 0.0633\n",
      "----------\n",
      "epoch 432/600\n",
      "1/16, train_loss: 0.0451\n",
      "2/16, train_loss: 0.0486\n",
      "3/16, train_loss: 0.0609\n",
      "4/16, train_loss: 0.0695\n",
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      "11/16, train_loss: 0.0724\n",
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      "15/16, train_loss: 0.0993\n",
      "16/16, train_loss: 0.0624\n",
      "17/16, train_loss: 0.0486\n",
      "epoch 432 average loss: 0.0660\n",
      "----------\n",
      "epoch 433/600\n",
      "1/16, train_loss: 0.0526\n",
      "2/16, train_loss: 0.0526\n",
      "3/16, train_loss: 0.0458\n",
      "4/16, train_loss: 0.0599\n",
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      "15/16, train_loss: 0.0605\n",
      "16/16, train_loss: 0.0601\n",
      "17/16, train_loss: 0.1180\n",
      "epoch 433 average loss: 0.0655\n",
      "----------\n",
      "epoch 434/600\n",
      "1/16, train_loss: 0.0482\n",
      "2/16, train_loss: 0.0704\n",
      "3/16, train_loss: 0.0561\n",
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      "17/16, train_loss: 0.0457\n",
      "epoch 434 average loss: 0.0673\n",
      "----------\n",
      "epoch 435/600\n",
      "1/16, train_loss: 0.0549\n",
      "2/16, train_loss: 0.0565\n",
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      "16/16, train_loss: 0.0778\n",
      "17/16, train_loss: 0.0568\n",
      "epoch 435 average loss: 0.0705\n",
      "current epoch: 435 current mean dice: 0.8553 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 436/600\n",
      "1/16, train_loss: 0.0484\n",
      "2/16, train_loss: 0.0448\n",
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      "epoch 436 average loss: 0.0605\n",
      "----------\n",
      "epoch 437/600\n",
      "1/16, train_loss: 0.0493\n",
      "2/16, train_loss: 0.0463\n",
      "3/16, train_loss: 0.0649\n",
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      "epoch 437 average loss: 0.0652\n",
      "----------\n",
      "epoch 438/600\n",
      "1/16, train_loss: 0.0536\n",
      "2/16, train_loss: 0.0479\n",
      "3/16, train_loss: 0.0697\n",
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      "15/16, train_loss: 0.0589\n",
      "16/16, train_loss: 0.0643\n",
      "17/16, train_loss: 0.0788\n",
      "epoch 438 average loss: 0.0690\n",
      "----------\n",
      "epoch 439/600\n",
      "1/16, train_loss: 0.0665\n",
      "2/16, train_loss: 0.0496\n",
      "3/16, train_loss: 0.0949\n",
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      "17/16, train_loss: 0.0754\n",
      "epoch 439 average loss: 0.0713\n",
      "----------\n",
      "epoch 440/600\n",
      "1/16, train_loss: 0.0756\n",
      "2/16, train_loss: 0.0504\n",
      "3/16, train_loss: 0.0709\n",
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      "7/16, train_loss: 0.0687\n",
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      "16/16, train_loss: 0.0746\n",
      "17/16, train_loss: 0.0535\n",
      "epoch 440 average loss: 0.0716\n",
      "current epoch: 440 current mean dice: 0.8586 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 441/600\n",
      "1/16, train_loss: 0.0498\n",
      "2/16, train_loss: 0.0466\n",
      "3/16, train_loss: 0.0633\n",
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      "17/16, train_loss: 0.0727\n",
      "epoch 441 average loss: 0.0656\n",
      "----------\n",
      "epoch 442/600\n",
      "1/16, train_loss: 0.0674\n",
      "2/16, train_loss: 0.0452\n",
      "3/16, train_loss: 0.0489\n",
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      "15/16, train_loss: 0.0594\n",
      "16/16, train_loss: 0.0662\n",
      "17/16, train_loss: 0.0613\n",
      "epoch 442 average loss: 0.0650\n",
      "----------\n",
      "epoch 443/600\n",
      "1/16, train_loss: 0.0494\n",
      "2/16, train_loss: 0.0505\n",
      "3/16, train_loss: 0.0565\n",
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      "14/16, train_loss: 0.0683\n",
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      "16/16, train_loss: 0.0651\n",
      "17/16, train_loss: 0.0722\n",
      "epoch 443 average loss: 0.0676\n",
      "----------\n",
      "epoch 444/600\n",
      "1/16, train_loss: 0.0548\n",
      "2/16, train_loss: 0.0537\n",
      "3/16, train_loss: 0.0951\n",
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      "15/16, train_loss: 0.0734\n",
      "16/16, train_loss: 0.0792\n",
      "17/16, train_loss: 0.0571\n",
      "epoch 444 average loss: 0.0687\n",
      "----------\n",
      "epoch 445/600\n",
      "1/16, train_loss: 0.0552\n",
      "2/16, train_loss: 0.0416\n",
      "3/16, train_loss: 0.0609\n",
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      "15/16, train_loss: 0.0694\n",
      "16/16, train_loss: 0.0574\n",
      "17/16, train_loss: 0.0487\n",
      "epoch 445 average loss: 0.0635\n",
      "current epoch: 445 current mean dice: 0.8630 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 446/600\n",
      "1/16, train_loss: 0.0638\n",
      "2/16, train_loss: 0.0449\n",
      "3/16, train_loss: 0.0512\n",
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      "16/16, train_loss: 0.0618\n",
      "17/16, train_loss: 0.0491\n",
      "epoch 446 average loss: 0.0633\n",
      "----------\n",
      "epoch 447/600\n",
      "1/16, train_loss: 0.0438\n",
      "2/16, train_loss: 0.0525\n",
      "3/16, train_loss: 0.0527\n",
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      "16/16, train_loss: 0.0684\n",
      "17/16, train_loss: 0.0631\n",
      "epoch 447 average loss: 0.0606\n",
      "----------\n",
      "epoch 448/600\n",
      "1/16, train_loss: 0.0548\n",
      "2/16, train_loss: 0.0532\n",
      "3/16, train_loss: 0.0676\n",
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      "16/16, train_loss: 0.0555\n",
      "17/16, train_loss: 0.0788\n",
      "epoch 448 average loss: 0.0647\n",
      "----------\n",
      "epoch 449/600\n",
      "1/16, train_loss: 0.0535\n",
      "2/16, train_loss: 0.0661\n",
      "3/16, train_loss: 0.0553\n",
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      "16/16, train_loss: 0.0612\n",
      "17/16, train_loss: 0.0580\n",
      "epoch 449 average loss: 0.0612\n",
      "----------\n",
      "epoch 450/600\n",
      "1/16, train_loss: 0.0458\n",
      "2/16, train_loss: 0.0516\n",
      "3/16, train_loss: 0.0621\n",
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      "16/16, train_loss: 0.0584\n",
      "17/16, train_loss: 0.0434\n",
      "epoch 450 average loss: 0.0640\n",
      "current epoch: 450 current mean dice: 0.8564 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 451/600\n",
      "1/16, train_loss: 0.0520\n",
      "2/16, train_loss: 0.0445\n",
      "3/16, train_loss: 0.1186\n",
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      "16/16, train_loss: 0.0691\n",
      "17/16, train_loss: 0.0656\n",
      "epoch 451 average loss: 0.0639\n",
      "----------\n",
      "epoch 452/600\n",
      "1/16, train_loss: 0.0456\n",
      "2/16, train_loss: 0.0453\n",
      "3/16, train_loss: 0.0830\n",
      "4/16, train_loss: 0.0546\n",
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      "16/16, train_loss: 0.0589\n",
      "17/16, train_loss: 0.0672\n",
      "epoch 452 average loss: 0.0612\n",
      "----------\n",
      "epoch 453/600\n",
      "1/16, train_loss: 0.0490\n",
      "2/16, train_loss: 0.0462\n",
      "3/16, train_loss: 0.0679\n",
      "4/16, train_loss: 0.0658\n",
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      "16/16, train_loss: 0.0595\n",
      "17/16, train_loss: 0.0526\n",
      "epoch 453 average loss: 0.0637\n",
      "----------\n",
      "epoch 454/600\n",
      "1/16, train_loss: 0.0737\n",
      "2/16, train_loss: 0.0573\n",
      "3/16, train_loss: 0.0906\n",
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      "16/16, train_loss: 0.0951\n",
      "17/16, train_loss: 0.0606\n",
      "epoch 454 average loss: 0.0683\n",
      "----------\n",
      "epoch 455/600\n",
      "1/16, train_loss: 0.0451\n",
      "2/16, train_loss: 0.0452\n",
      "3/16, train_loss: 0.1094\n",
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      "16/16, train_loss: 0.0644\n",
      "17/16, train_loss: 0.0537\n",
      "epoch 455 average loss: 0.0673\n",
      "current epoch: 455 current mean dice: 0.8517 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 456/600\n",
      "1/16, train_loss: 0.0442\n",
      "2/16, train_loss: 0.0496\n",
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      "epoch 456 average loss: 0.0649\n",
      "----------\n",
      "epoch 457/600\n",
      "1/16, train_loss: 0.0501\n",
      "2/16, train_loss: 0.0448\n",
      "3/16, train_loss: 0.0694\n",
      "4/16, train_loss: 0.0736\n",
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      "16/16, train_loss: 0.0681\n",
      "17/16, train_loss: 0.0993\n",
      "epoch 457 average loss: 0.0687\n",
      "----------\n",
      "epoch 458/600\n",
      "1/16, train_loss: 0.0481\n",
      "2/16, train_loss: 0.0527\n",
      "3/16, train_loss: 0.0643\n",
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      "15/16, train_loss: 0.0738\n",
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      "17/16, train_loss: 0.0639\n",
      "epoch 458 average loss: 0.0649\n",
      "----------\n",
      "epoch 459/600\n",
      "1/16, train_loss: 0.0546\n",
      "2/16, train_loss: 0.0490\n",
      "3/16, train_loss: 0.0611\n",
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      "epoch 459 average loss: 0.0605\n",
      "----------\n",
      "epoch 460/600\n",
      "1/16, train_loss: 0.0508\n",
      "2/16, train_loss: 0.0514\n",
      "3/16, train_loss: 0.0824\n",
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      "16/16, train_loss: 0.0682\n",
      "17/16, train_loss: 0.0407\n",
      "epoch 460 average loss: 0.0604\n",
      "current epoch: 460 current mean dice: 0.8629 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 461/600\n",
      "1/16, train_loss: 0.0738\n",
      "2/16, train_loss: 0.0467\n",
      "3/16, train_loss: 0.0592\n",
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      "15/16, train_loss: 0.0585\n",
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      "17/16, train_loss: 0.0433\n",
      "epoch 461 average loss: 0.0677\n",
      "----------\n",
      "epoch 462/600\n",
      "1/16, train_loss: 0.0579\n",
      "2/16, train_loss: 0.0670\n",
      "3/16, train_loss: 0.0860\n",
      "4/16, train_loss: 0.0688\n",
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      "11/16, train_loss: 0.0629\n",
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      "16/16, train_loss: 0.0638\n",
      "17/16, train_loss: 0.0518\n",
      "epoch 462 average loss: 0.0659\n",
      "----------\n",
      "epoch 463/600\n",
      "1/16, train_loss: 0.0586\n",
      "2/16, train_loss: 0.0529\n",
      "3/16, train_loss: 0.0548\n",
      "4/16, train_loss: 0.0673\n",
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      "11/16, train_loss: 0.1014\n",
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      "16/16, train_loss: 0.0727\n",
      "17/16, train_loss: 0.1149\n",
      "epoch 463 average loss: 0.0679\n",
      "----------\n",
      "epoch 464/600\n",
      "1/16, train_loss: 0.0432\n",
      "2/16, train_loss: 0.0687\n",
      "3/16, train_loss: 0.0646\n",
      "4/16, train_loss: 0.0560\n",
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      "11/16, train_loss: 0.0753\n",
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      "15/16, train_loss: 0.0536\n",
      "16/16, train_loss: 0.0905\n",
      "17/16, train_loss: 0.0671\n",
      "epoch 464 average loss: 0.0681\n",
      "----------\n",
      "epoch 465/600\n",
      "1/16, train_loss: 0.0515\n",
      "2/16, train_loss: 0.0444\n",
      "3/16, train_loss: 0.0520\n",
      "4/16, train_loss: 0.0585\n",
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      "6/16, train_loss: 0.0692\n",
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      "11/16, train_loss: 0.0668\n",
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      "13/16, train_loss: 0.0659\n",
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      "15/16, train_loss: 0.0750\n",
      "16/16, train_loss: 0.0679\n",
      "17/16, train_loss: 0.0523\n",
      "epoch 465 average loss: 0.0620\n",
      "current epoch: 465 current mean dice: 0.8658 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 466/600\n",
      "1/16, train_loss: 0.0488\n",
      "2/16, train_loss: 0.0478\n",
      "3/16, train_loss: 0.0617\n",
      "4/16, train_loss: 0.0635\n",
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      "15/16, train_loss: 0.0517\n",
      "16/16, train_loss: 0.0627\n",
      "17/16, train_loss: 0.0588\n",
      "epoch 466 average loss: 0.0597\n",
      "----------\n",
      "epoch 467/600\n",
      "1/16, train_loss: 0.0564\n",
      "2/16, train_loss: 0.0420\n",
      "3/16, train_loss: 0.1962\n",
      "4/16, train_loss: 0.0585\n",
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      "11/16, train_loss: 0.0643\n",
      "12/16, train_loss: 0.0679\n",
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      "14/16, train_loss: 0.0640\n",
      "15/16, train_loss: 0.0524\n",
      "16/16, train_loss: 0.0824\n",
      "17/16, train_loss: 0.0507\n",
      "epoch 467 average loss: 0.0705\n",
      "----------\n",
      "epoch 468/600\n",
      "1/16, train_loss: 0.0644\n",
      "2/16, train_loss: 0.0589\n",
      "3/16, train_loss: 0.0548\n",
      "4/16, train_loss: 0.0679\n",
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      "6/16, train_loss: 0.0679\n",
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      "11/16, train_loss: 0.0577\n",
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      "14/16, train_loss: 0.0738\n",
      "15/16, train_loss: 0.0529\n",
      "16/16, train_loss: 0.0690\n",
      "17/16, train_loss: 0.0512\n",
      "epoch 468 average loss: 0.0621\n",
      "----------\n",
      "epoch 469/600\n",
      "1/16, train_loss: 0.0444\n",
      "2/16, train_loss: 0.0413\n",
      "3/16, train_loss: 0.0702\n",
      "4/16, train_loss: 0.0665\n",
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      "6/16, train_loss: 0.0647\n",
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      "11/16, train_loss: 0.0842\n",
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      "15/16, train_loss: 0.0563\n",
      "16/16, train_loss: 0.0606\n",
      "17/16, train_loss: 0.0803\n",
      "epoch 469 average loss: 0.0647\n",
      "----------\n",
      "epoch 470/600\n",
      "1/16, train_loss: 0.0551\n",
      "2/16, train_loss: 0.0488\n",
      "3/16, train_loss: 0.0577\n",
      "4/16, train_loss: 0.0704\n",
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      "15/16, train_loss: 0.0501\n",
      "16/16, train_loss: 0.0727\n",
      "17/16, train_loss: 0.0378\n",
      "epoch 470 average loss: 0.0622\n",
      "current epoch: 470 current mean dice: 0.8597 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 471/600\n",
      "1/16, train_loss: 0.0553\n",
      "2/16, train_loss: 0.0469\n",
      "3/16, train_loss: 0.0532\n",
      "4/16, train_loss: 0.0692\n",
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      "15/16, train_loss: 0.0652\n",
      "16/16, train_loss: 0.0790\n",
      "17/16, train_loss: 0.1430\n",
      "epoch 471 average loss: 0.0711\n",
      "----------\n",
      "epoch 472/600\n",
      "1/16, train_loss: 0.0521\n",
      "2/16, train_loss: 0.0656\n",
      "3/16, train_loss: 0.0654\n",
      "4/16, train_loss: 0.0903\n",
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      "11/16, train_loss: 0.0581\n",
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      "15/16, train_loss: 0.0570\n",
      "16/16, train_loss: 0.0704\n",
      "17/16, train_loss: 0.0599\n",
      "epoch 472 average loss: 0.0667\n",
      "----------\n",
      "epoch 473/600\n",
      "1/16, train_loss: 0.0492\n",
      "2/16, train_loss: 0.0570\n",
      "3/16, train_loss: 0.1500\n",
      "4/16, train_loss: 0.0716\n",
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      "11/16, train_loss: 0.0737\n",
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      "14/16, train_loss: 0.0592\n",
      "15/16, train_loss: 0.0614\n",
      "16/16, train_loss: 0.0902\n",
      "17/16, train_loss: 0.0943\n",
      "epoch 473 average loss: 0.0725\n",
      "----------\n",
      "epoch 474/600\n",
      "1/16, train_loss: 0.0498\n",
      "2/16, train_loss: 0.0441\n",
      "3/16, train_loss: 0.0626\n",
      "4/16, train_loss: 0.0697\n",
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      "6/16, train_loss: 0.0673\n",
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      "11/16, train_loss: 0.0726\n",
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      "14/16, train_loss: 0.0630\n",
      "15/16, train_loss: 0.0573\n",
      "16/16, train_loss: 0.0874\n",
      "17/16, train_loss: 0.0714\n",
      "epoch 474 average loss: 0.0691\n",
      "----------\n",
      "epoch 475/600\n",
      "1/16, train_loss: 0.0552\n",
      "2/16, train_loss: 0.0467\n",
      "3/16, train_loss: 0.0495\n",
      "4/16, train_loss: 0.0568\n",
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      "11/16, train_loss: 0.0830\n",
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      "15/16, train_loss: 0.0530\n",
      "16/16, train_loss: 0.0516\n",
      "17/16, train_loss: 0.0420\n",
      "epoch 475 average loss: 0.0593\n",
      "current epoch: 475 current mean dice: 0.8574 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 476/600\n",
      "1/16, train_loss: 0.0507\n",
      "2/16, train_loss: 0.0403\n",
      "3/16, train_loss: 0.0713\n",
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      "15/16, train_loss: 0.0674\n",
      "16/16, train_loss: 0.0615\n",
      "17/16, train_loss: 0.0448\n",
      "epoch 476 average loss: 0.0619\n",
      "----------\n",
      "epoch 477/600\n",
      "1/16, train_loss: 0.0612\n",
      "2/16, train_loss: 0.0452\n",
      "3/16, train_loss: 0.0845\n",
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      "16/16, train_loss: 0.0670\n",
      "17/16, train_loss: 0.0415\n",
      "epoch 477 average loss: 0.0628\n",
      "----------\n",
      "epoch 478/600\n",
      "1/16, train_loss: 0.0450\n",
      "2/16, train_loss: 0.0515\n",
      "3/16, train_loss: 0.0892\n",
      "4/16, train_loss: 0.0519\n",
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      "15/16, train_loss: 0.0664\n",
      "16/16, train_loss: 0.0574\n",
      "17/16, train_loss: 0.0544\n",
      "epoch 478 average loss: 0.0628\n",
      "----------\n",
      "epoch 479/600\n",
      "1/16, train_loss: 0.0521\n",
      "2/16, train_loss: 0.0468\n",
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      "16/16, train_loss: 0.0646\n",
      "17/16, train_loss: 0.0426\n",
      "epoch 479 average loss: 0.0669\n",
      "----------\n",
      "epoch 480/600\n",
      "1/16, train_loss: 0.0541\n",
      "2/16, train_loss: 0.0535\n",
      "3/16, train_loss: 0.0851\n",
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      "16/16, train_loss: 0.0671\n",
      "17/16, train_loss: 0.0485\n",
      "epoch 480 average loss: 0.0624\n",
      "current epoch: 480 current mean dice: 0.8712 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 481/600\n",
      "1/16, train_loss: 0.0454\n",
      "2/16, train_loss: 0.0489\n",
      "3/16, train_loss: 0.0748\n",
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      "epoch 481 average loss: 0.0647\n",
      "----------\n",
      "epoch 482/600\n",
      "1/16, train_loss: 0.0452\n",
      "2/16, train_loss: 0.0473\n",
      "3/16, train_loss: 0.1170\n",
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      "17/16, train_loss: 0.0647\n",
      "epoch 482 average loss: 0.0673\n",
      "----------\n",
      "epoch 483/600\n",
      "1/16, train_loss: 0.0503\n",
      "2/16, train_loss: 0.0539\n",
      "3/16, train_loss: 0.0748\n",
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      "15/16, train_loss: 0.0568\n",
      "16/16, train_loss: 0.0598\n",
      "17/16, train_loss: 0.0602\n",
      "epoch 483 average loss: 0.0667\n",
      "----------\n",
      "epoch 484/600\n",
      "1/16, train_loss: 0.0465\n",
      "2/16, train_loss: 0.0533\n",
      "3/16, train_loss: 0.0754\n",
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      "11/16, train_loss: 0.1067\n",
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      "14/16, train_loss: 0.0873\n",
      "15/16, train_loss: 0.0744\n",
      "16/16, train_loss: 0.0577\n",
      "17/16, train_loss: 0.0739\n",
      "epoch 484 average loss: 0.0707\n",
      "----------\n",
      "epoch 485/600\n",
      "1/16, train_loss: 0.0518\n",
      "2/16, train_loss: 0.0547\n",
      "3/16, train_loss: 0.0566\n",
      "4/16, train_loss: 0.0754\n",
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      "14/16, train_loss: 0.0770\n",
      "15/16, train_loss: 0.0584\n",
      "16/16, train_loss: 0.0513\n",
      "17/16, train_loss: 0.0486\n",
      "epoch 485 average loss: 0.0670\n",
      "current epoch: 485 current mean dice: 0.8646 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 486/600\n",
      "1/16, train_loss: 0.0511\n",
      "2/16, train_loss: 0.0422\n",
      "3/16, train_loss: 0.0942\n",
      "4/16, train_loss: 0.0805\n",
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      "11/16, train_loss: 0.0683\n",
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      "14/16, train_loss: 0.0581\n",
      "15/16, train_loss: 0.0572\n",
      "16/16, train_loss: 0.0656\n",
      "17/16, train_loss: 0.0440\n",
      "epoch 486 average loss: 0.0690\n",
      "----------\n",
      "epoch 487/600\n",
      "1/16, train_loss: 0.0520\n",
      "2/16, train_loss: 0.0571\n",
      "3/16, train_loss: 0.0749\n",
      "4/16, train_loss: 0.0578\n",
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      "11/16, train_loss: 0.0659\n",
      "12/16, train_loss: 0.0793\n",
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      "14/16, train_loss: 0.0951\n",
      "15/16, train_loss: 0.0554\n",
      "16/16, train_loss: 0.0607\n",
      "17/16, train_loss: 0.0672\n",
      "epoch 487 average loss: 0.0644\n",
      "----------\n",
      "epoch 488/600\n",
      "1/16, train_loss: 0.0577\n",
      "2/16, train_loss: 0.0541\n",
      "3/16, train_loss: 0.0747\n",
      "4/16, train_loss: 0.0538\n",
      "5/16, train_loss: 0.0526\n",
      "6/16, train_loss: 0.0729\n",
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      "11/16, train_loss: 0.0875\n",
      "12/16, train_loss: 0.0876\n",
      "13/16, train_loss: 0.0651\n",
      "14/16, train_loss: 0.0494\n",
      "15/16, train_loss: 0.0505\n",
      "16/16, train_loss: 0.0643\n",
      "17/16, train_loss: 0.0516\n",
      "epoch 488 average loss: 0.0676\n",
      "----------\n",
      "epoch 489/600\n",
      "1/16, train_loss: 0.0665\n",
      "2/16, train_loss: 0.0920\n",
      "3/16, train_loss: 0.0795\n",
      "4/16, train_loss: 0.0679\n",
      "5/16, train_loss: 0.0961\n",
      "6/16, train_loss: 0.0749\n",
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      "11/16, train_loss: 0.0608\n",
      "12/16, train_loss: 0.0838\n",
      "13/16, train_loss: 0.0748\n",
      "14/16, train_loss: 0.0554\n",
      "15/16, train_loss: 0.0535\n",
      "16/16, train_loss: 0.0916\n",
      "17/16, train_loss: 0.0612\n",
      "epoch 489 average loss: 0.0709\n",
      "----------\n",
      "epoch 490/600\n",
      "1/16, train_loss: 0.0543\n",
      "2/16, train_loss: 0.0509\n",
      "3/16, train_loss: 0.0444\n",
      "4/16, train_loss: 0.0477\n",
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      "6/16, train_loss: 0.0868\n",
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      "10/16, train_loss: 0.0651\n",
      "11/16, train_loss: 0.0791\n",
      "12/16, train_loss: 0.0932\n",
      "13/16, train_loss: 0.0689\n",
      "14/16, train_loss: 0.0772\n",
      "15/16, train_loss: 0.0560\n",
      "16/16, train_loss: 0.0573\n",
      "17/16, train_loss: 0.0554\n",
      "epoch 490 average loss: 0.0632\n",
      "current epoch: 490 current mean dice: 0.8626 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 491/600\n",
      "1/16, train_loss: 0.0454\n",
      "2/16, train_loss: 0.0494\n",
      "3/16, train_loss: 0.0642\n",
      "4/16, train_loss: 0.0813\n",
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      "11/16, train_loss: 0.0738\n",
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      "14/16, train_loss: 0.0662\n",
      "15/16, train_loss: 0.0622\n",
      "16/16, train_loss: 0.0641\n",
      "17/16, train_loss: 0.0835\n",
      "epoch 491 average loss: 0.0633\n",
      "----------\n",
      "epoch 492/600\n",
      "1/16, train_loss: 0.0557\n",
      "2/16, train_loss: 0.0589\n",
      "3/16, train_loss: 0.0617\n",
      "4/16, train_loss: 0.0551\n",
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      "15/16, train_loss: 0.0617\n",
      "16/16, train_loss: 0.0742\n",
      "17/16, train_loss: 0.0617\n",
      "epoch 492 average loss: 0.0634\n",
      "----------\n",
      "epoch 493/600\n",
      "1/16, train_loss: 0.0648\n",
      "2/16, train_loss: 0.0521\n",
      "3/16, train_loss: 0.0824\n",
      "4/16, train_loss: 0.0641\n",
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      "11/16, train_loss: 0.0737\n",
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      "13/16, train_loss: 0.0874\n",
      "14/16, train_loss: 0.0620\n",
      "15/16, train_loss: 0.0505\n",
      "16/16, train_loss: 0.0611\n",
      "17/16, train_loss: 0.0575\n",
      "epoch 493 average loss: 0.0646\n",
      "----------\n",
      "epoch 494/600\n",
      "1/16, train_loss: 0.0453\n",
      "2/16, train_loss: 0.0471\n",
      "3/16, train_loss: 0.0642\n",
      "4/16, train_loss: 0.0696\n",
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      "11/16, train_loss: 0.0689\n",
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      "13/16, train_loss: 0.0531\n",
      "14/16, train_loss: 0.0646\n",
      "15/16, train_loss: 0.1164\n",
      "16/16, train_loss: 0.0631\n",
      "17/16, train_loss: 0.0406\n",
      "epoch 494 average loss: 0.0635\n",
      "----------\n",
      "epoch 495/600\n",
      "1/16, train_loss: 0.0494\n",
      "2/16, train_loss: 0.0571\n",
      "3/16, train_loss: 0.0665\n",
      "4/16, train_loss: 0.0692\n",
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      "6/16, train_loss: 0.0586\n",
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      "11/16, train_loss: 0.0760\n",
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      "13/16, train_loss: 0.0753\n",
      "14/16, train_loss: 0.0536\n",
      "15/16, train_loss: 0.0674\n",
      "16/16, train_loss: 0.0690\n",
      "17/16, train_loss: 0.0584\n",
      "epoch 495 average loss: 0.0633\n",
      "current epoch: 495 current mean dice: 0.8675 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 496/600\n",
      "1/16, train_loss: 0.0457\n",
      "2/16, train_loss: 0.0473\n",
      "3/16, train_loss: 0.0559\n",
      "4/16, train_loss: 0.0573\n",
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      "9/16, train_loss: 0.0647\n",
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      "11/16, train_loss: 0.1038\n",
      "12/16, train_loss: 0.0716\n",
      "13/16, train_loss: 0.0584\n",
      "14/16, train_loss: 0.0648\n",
      "15/16, train_loss: 0.0589\n",
      "16/16, train_loss: 0.0660\n",
      "17/16, train_loss: 0.0518\n",
      "epoch 496 average loss: 0.0622\n",
      "----------\n",
      "epoch 497/600\n",
      "1/16, train_loss: 0.0775\n",
      "2/16, train_loss: 0.0571\n",
      "3/16, train_loss: 0.0653\n",
      "4/16, train_loss: 0.0748\n",
      "5/16, train_loss: 0.0527\n",
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      "11/16, train_loss: 0.0488\n",
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      "15/16, train_loss: 0.0637\n",
      "16/16, train_loss: 0.0572\n",
      "17/16, train_loss: 0.0430\n",
      "epoch 497 average loss: 0.0607\n",
      "----------\n",
      "epoch 498/600\n",
      "1/16, train_loss: 0.0491\n",
      "2/16, train_loss: 0.0452\n",
      "3/16, train_loss: 0.0702\n",
      "4/16, train_loss: 0.0559\n",
      "5/16, train_loss: 0.0569\n",
      "6/16, train_loss: 0.0642\n",
      "7/16, train_loss: 0.0657\n",
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      "10/16, train_loss: 0.0654\n",
      "11/16, train_loss: 0.0640\n",
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      "15/16, train_loss: 0.0658\n",
      "16/16, train_loss: 0.0689\n",
      "17/16, train_loss: 0.0651\n",
      "epoch 498 average loss: 0.0638\n",
      "----------\n",
      "epoch 499/600\n",
      "1/16, train_loss: 0.0454\n",
      "2/16, train_loss: 0.0581\n",
      "3/16, train_loss: 0.0589\n",
      "4/16, train_loss: 0.0730\n",
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      "11/16, train_loss: 0.0743\n",
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      "13/16, train_loss: 0.0673\n",
      "14/16, train_loss: 0.0708\n",
      "15/16, train_loss: 0.0591\n",
      "16/16, train_loss: 0.0646\n",
      "17/16, train_loss: 0.0551\n",
      "epoch 499 average loss: 0.0656\n",
      "----------\n",
      "epoch 500/600\n",
      "1/16, train_loss: 0.0502\n",
      "2/16, train_loss: 0.0571\n",
      "3/16, train_loss: 0.0766\n",
      "4/16, train_loss: 0.0684\n",
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      "7/16, train_loss: 0.0654\n",
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      "16/16, train_loss: 0.0726\n",
      "17/16, train_loss: 0.0845\n",
      "epoch 500 average loss: 0.0650\n",
      "current epoch: 500 current mean dice: 0.8538 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 501/600\n",
      "1/16, train_loss: 0.0460\n",
      "2/16, train_loss: 0.0539\n",
      "3/16, train_loss: 0.1027\n",
      "4/16, train_loss: 0.0753\n",
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      "17/16, train_loss: 0.0735\n",
      "epoch 501 average loss: 0.0694\n",
      "----------\n",
      "epoch 502/600\n",
      "1/16, train_loss: 0.0546\n",
      "2/16, train_loss: 0.0565\n",
      "3/16, train_loss: 0.0679\n",
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      "17/16, train_loss: 0.0433\n",
      "epoch 502 average loss: 0.0607\n",
      "----------\n",
      "epoch 503/600\n",
      "1/16, train_loss: 0.0555\n",
      "2/16, train_loss: 0.0442\n",
      "3/16, train_loss: 0.0549\n",
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      "17/16, train_loss: 0.0460\n",
      "epoch 503 average loss: 0.0611\n",
      "----------\n",
      "epoch 504/600\n",
      "1/16, train_loss: 0.0503\n",
      "2/16, train_loss: 0.0472\n",
      "3/16, train_loss: 0.0522\n",
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      "17/16, train_loss: 0.0593\n",
      "epoch 504 average loss: 0.0622\n",
      "----------\n",
      "epoch 505/600\n",
      "1/16, train_loss: 0.0434\n",
      "2/16, train_loss: 0.0429\n",
      "3/16, train_loss: 0.0687\n",
      "4/16, train_loss: 0.0654\n",
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      "13/16, train_loss: 0.0589\n",
      "14/16, train_loss: 0.0646\n",
      "15/16, train_loss: 0.0500\n",
      "16/16, train_loss: 0.0827\n",
      "17/16, train_loss: 0.0550\n",
      "epoch 505 average loss: 0.0599\n",
      "current epoch: 505 current mean dice: 0.8518 \n",
      "best mean dice: 0.8716  at epoch: 415\n",
      "----------\n",
      "epoch 506/600\n",
      "1/16, train_loss: 0.0713\n",
      "2/16, train_loss: 0.0627\n",
      "3/16, train_loss: 0.0734\n",
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      "9/16, train_loss: 0.0535\n",
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      "15/16, train_loss: 0.0565\n",
      "16/16, train_loss: 0.0531\n",
      "17/16, train_loss: 0.0578\n",
      "epoch 506 average loss: 0.0621\n",
      "----------\n",
      "epoch 507/600\n",
      "1/16, train_loss: 0.0469\n",
      "2/16, train_loss: 0.0535\n",
      "3/16, train_loss: 0.0538\n",
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      "16/16, train_loss: 0.0599\n",
      "17/16, train_loss: 0.0479\n",
      "epoch 507 average loss: 0.0613\n",
      "----------\n",
      "epoch 508/600\n",
      "1/16, train_loss: 0.0453\n",
      "2/16, train_loss: 0.0398\n",
      "3/16, train_loss: 0.0871\n",
      "4/16, train_loss: 0.0648\n",
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      "6/16, train_loss: 0.0698\n",
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      "15/16, train_loss: 0.0680\n",
      "16/16, train_loss: 0.0637\n",
      "17/16, train_loss: 0.0811\n",
      "epoch 508 average loss: 0.0660\n",
      "----------\n",
      "epoch 509/600\n",
      "1/16, train_loss: 0.0463\n",
      "2/16, train_loss: 0.0510\n",
      "3/16, train_loss: 0.0506\n",
      "4/16, train_loss: 0.0698\n",
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      "12/16, train_loss: 0.0644\n",
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      "15/16, train_loss: 0.0643\n",
      "16/16, train_loss: 0.0657\n",
      "17/16, train_loss: 0.0560\n",
      "epoch 509 average loss: 0.0592\n",
      "----------\n",
      "epoch 510/600\n",
      "1/16, train_loss: 0.0507\n",
      "2/16, train_loss: 0.0499\n",
      "3/16, train_loss: 0.0899\n",
      "4/16, train_loss: 0.0658\n",
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      "6/16, train_loss: 0.0709\n",
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      "10/16, train_loss: 0.0656\n",
      "11/16, train_loss: 0.0829\n",
      "12/16, train_loss: 0.0849\n",
      "13/16, train_loss: 0.0646\n",
      "14/16, train_loss: 0.0644\n",
      "15/16, train_loss: 0.0703\n",
      "16/16, train_loss: 0.0743\n",
      "17/16, train_loss: 0.0601\n",
      "epoch 510 average loss: 0.0673\n",
      "saved new best metric model at the 510th epoch\n",
      "current epoch: 510 current mean dice: 0.8783 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 511/600\n",
      "1/16, train_loss: 0.0644\n",
      "2/16, train_loss: 0.0469\n",
      "3/16, train_loss: 0.0660\n",
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      "15/16, train_loss: 0.0570\n",
      "16/16, train_loss: 0.0726\n",
      "17/16, train_loss: 0.0558\n",
      "epoch 511 average loss: 0.0633\n",
      "----------\n",
      "epoch 512/600\n",
      "1/16, train_loss: 0.0513\n",
      "2/16, train_loss: 0.0502\n",
      "3/16, train_loss: 0.0554\n",
      "4/16, train_loss: 0.0719\n",
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      "11/16, train_loss: 0.0728\n",
      "12/16, train_loss: 0.0898\n",
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      "15/16, train_loss: 0.0522\n",
      "16/16, train_loss: 0.0624\n",
      "17/16, train_loss: 0.0653\n",
      "epoch 512 average loss: 0.0619\n",
      "----------\n",
      "epoch 513/600\n",
      "1/16, train_loss: 0.0564\n",
      "2/16, train_loss: 0.0480\n",
      "3/16, train_loss: 0.0521\n",
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      "11/16, train_loss: 0.0679\n",
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      "14/16, train_loss: 0.0677\n",
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      "17/16, train_loss: 0.0338\n",
      "epoch 513 average loss: 0.0608\n",
      "----------\n",
      "epoch 514/600\n",
      "1/16, train_loss: 0.0579\n",
      "2/16, train_loss: 0.0421\n",
      "3/16, train_loss: 0.0837\n",
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      "16/16, train_loss: 0.0797\n",
      "17/16, train_loss: 0.0516\n",
      "epoch 514 average loss: 0.0643\n",
      "----------\n",
      "epoch 515/600\n",
      "1/16, train_loss: 0.0581\n",
      "2/16, train_loss: 0.0465\n",
      "3/16, train_loss: 0.0883\n",
      "4/16, train_loss: 0.0769\n",
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      "11/16, train_loss: 0.0726\n",
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      "13/16, train_loss: 0.0659\n",
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      "16/16, train_loss: 0.0628\n",
      "17/16, train_loss: 0.0974\n",
      "epoch 515 average loss: 0.0659\n",
      "current epoch: 515 current mean dice: 0.8619 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 516/600\n",
      "1/16, train_loss: 0.0479\n",
      "2/16, train_loss: 0.0459\n",
      "3/16, train_loss: 0.1493\n",
      "4/16, train_loss: 0.0782\n",
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      "11/16, train_loss: 0.0724\n",
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      "14/16, train_loss: 0.0916\n",
      "15/16, train_loss: 0.0600\n",
      "16/16, train_loss: 0.0593\n",
      "17/16, train_loss: 0.0431\n",
      "epoch 516 average loss: 0.0690\n",
      "----------\n",
      "epoch 517/600\n",
      "1/16, train_loss: 0.0528\n",
      "2/16, train_loss: 0.0427\n",
      "3/16, train_loss: 0.0645\n",
      "4/16, train_loss: 0.0684\n",
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      "12/16, train_loss: 0.0735\n",
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      "14/16, train_loss: 0.0643\n",
      "15/16, train_loss: 0.0538\n",
      "16/16, train_loss: 0.0636\n",
      "17/16, train_loss: 0.0518\n",
      "epoch 517 average loss: 0.0638\n",
      "----------\n",
      "epoch 518/600\n",
      "1/16, train_loss: 0.0468\n",
      "2/16, train_loss: 0.0461\n",
      "3/16, train_loss: 0.0590\n",
      "4/16, train_loss: 0.0593\n",
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      "11/16, train_loss: 0.0781\n",
      "12/16, train_loss: 0.0839\n",
      "13/16, train_loss: 0.0659\n",
      "14/16, train_loss: 0.0627\n",
      "15/16, train_loss: 0.0616\n",
      "16/16, train_loss: 0.0769\n",
      "17/16, train_loss: 0.0433\n",
      "epoch 518 average loss: 0.0596\n",
      "----------\n",
      "epoch 519/600\n",
      "1/16, train_loss: 0.0507\n",
      "2/16, train_loss: 0.0489\n",
      "3/16, train_loss: 0.0526\n",
      "4/16, train_loss: 0.0637\n",
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      "11/16, train_loss: 0.0709\n",
      "12/16, train_loss: 0.0698\n",
      "13/16, train_loss: 0.0660\n",
      "14/16, train_loss: 0.0625\n",
      "15/16, train_loss: 0.0862\n",
      "16/16, train_loss: 0.0594\n",
      "17/16, train_loss: 0.0455\n",
      "epoch 519 average loss: 0.0633\n",
      "----------\n",
      "epoch 520/600\n",
      "1/16, train_loss: 0.0472\n",
      "2/16, train_loss: 0.0419\n",
      "3/16, train_loss: 0.1220\n",
      "4/16, train_loss: 0.0603\n",
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      "6/16, train_loss: 0.0576\n",
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      "10/16, train_loss: 0.0684\n",
      "11/16, train_loss: 0.0620\n",
      "12/16, train_loss: 0.0702\n",
      "13/16, train_loss: 0.0676\n",
      "14/16, train_loss: 0.0546\n",
      "15/16, train_loss: 0.0717\n",
      "16/16, train_loss: 0.0643\n",
      "17/16, train_loss: 0.0606\n",
      "epoch 520 average loss: 0.0648\n",
      "current epoch: 520 current mean dice: 0.8696 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 521/600\n",
      "1/16, train_loss: 0.0578\n",
      "2/16, train_loss: 0.0426\n",
      "3/16, train_loss: 0.0988\n",
      "4/16, train_loss: 0.0563\n",
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      "11/16, train_loss: 0.0614\n",
      "12/16, train_loss: 0.0584\n",
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      "14/16, train_loss: 0.0761\n",
      "15/16, train_loss: 0.0471\n",
      "16/16, train_loss: 0.0619\n",
      "17/16, train_loss: 0.0576\n",
      "epoch 521 average loss: 0.0598\n",
      "----------\n",
      "epoch 522/600\n",
      "1/16, train_loss: 0.0482\n",
      "2/16, train_loss: 0.0454\n",
      "3/16, train_loss: 0.1089\n",
      "4/16, train_loss: 0.0671\n",
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      "11/16, train_loss: 0.0808\n",
      "12/16, train_loss: 0.0872\n",
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      "14/16, train_loss: 0.0669\n",
      "15/16, train_loss: 0.0527\n",
      "16/16, train_loss: 0.0819\n",
      "17/16, train_loss: 0.0674\n",
      "epoch 522 average loss: 0.0679\n",
      "----------\n",
      "epoch 523/600\n",
      "1/16, train_loss: 0.0506\n",
      "2/16, train_loss: 0.0401\n",
      "3/16, train_loss: 0.0860\n",
      "4/16, train_loss: 0.0654\n",
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      "epoch 523 average loss: 0.0702\n",
      "----------\n",
      "epoch 524/600\n",
      "1/16, train_loss: 0.0483\n",
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      "epoch 524 average loss: 0.0685\n",
      "----------\n",
      "epoch 525/600\n",
      "1/16, train_loss: 0.0473\n",
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      "17/16, train_loss: 0.0415\n",
      "epoch 525 average loss: 0.0638\n",
      "current epoch: 525 current mean dice: 0.8635 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 526/600\n",
      "1/16, train_loss: 0.0592\n",
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      "epoch 526 average loss: 0.0602\n",
      "----------\n",
      "epoch 527/600\n",
      "1/16, train_loss: 0.0462\n",
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      "epoch 527 average loss: 0.0594\n",
      "----------\n",
      "epoch 528/600\n",
      "1/16, train_loss: 0.0443\n",
      "2/16, train_loss: 0.0428\n",
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      "epoch 528 average loss: 0.0594\n",
      "----------\n",
      "epoch 529/600\n",
      "1/16, train_loss: 0.0497\n",
      "2/16, train_loss: 0.0560\n",
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      "epoch 529 average loss: 0.0647\n",
      "----------\n",
      "epoch 530/600\n",
      "1/16, train_loss: 0.0494\n",
      "2/16, train_loss: 0.0399\n",
      "3/16, train_loss: 0.0683\n",
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      "16/16, train_loss: 0.0622\n",
      "17/16, train_loss: 0.0398\n",
      "epoch 530 average loss: 0.0585\n",
      "current epoch: 530 current mean dice: 0.8495 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 531/600\n",
      "1/16, train_loss: 0.0427\n",
      "2/16, train_loss: 0.0476\n",
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      "epoch 531 average loss: 0.0581\n",
      "----------\n",
      "epoch 532/600\n",
      "1/16, train_loss: 0.0609\n",
      "2/16, train_loss: 0.0407\n",
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      "epoch 532 average loss: 0.0627\n",
      "----------\n",
      "epoch 533/600\n",
      "1/16, train_loss: 0.0601\n",
      "2/16, train_loss: 0.0555\n",
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      "16/16, train_loss: 0.0781\n",
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      "epoch 533 average loss: 0.0636\n",
      "----------\n",
      "epoch 534/600\n",
      "1/16, train_loss: 0.0473\n",
      "2/16, train_loss: 0.0526\n",
      "3/16, train_loss: 0.0481\n",
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      "16/16, train_loss: 0.0503\n",
      "17/16, train_loss: 0.0992\n",
      "epoch 534 average loss: 0.0672\n",
      "----------\n",
      "epoch 535/600\n",
      "1/16, train_loss: 0.0502\n",
      "2/16, train_loss: 0.0570\n",
      "3/16, train_loss: 0.0725\n",
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      "16/16, train_loss: 0.0600\n",
      "17/16, train_loss: 0.0455\n",
      "epoch 535 average loss: 0.0617\n",
      "current epoch: 535 current mean dice: 0.8654 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 536/600\n",
      "1/16, train_loss: 0.0574\n",
      "2/16, train_loss: 0.0606\n",
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      "17/16, train_loss: 0.0777\n",
      "epoch 536 average loss: 0.0601\n",
      "----------\n",
      "epoch 537/600\n",
      "1/16, train_loss: 0.0460\n",
      "2/16, train_loss: 0.0413\n",
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      "epoch 537 average loss: 0.0591\n",
      "----------\n",
      "epoch 538/600\n",
      "1/16, train_loss: 0.0507\n",
      "2/16, train_loss: 0.0490\n",
      "3/16, train_loss: 0.0595\n",
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      "epoch 538 average loss: 0.0620\n",
      "----------\n",
      "epoch 539/600\n",
      "1/16, train_loss: 0.0443\n",
      "2/16, train_loss: 0.0393\n",
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      "16/16, train_loss: 0.0802\n",
      "17/16, train_loss: 0.0396\n",
      "epoch 539 average loss: 0.0614\n",
      "----------\n",
      "epoch 540/600\n",
      "1/16, train_loss: 0.0534\n",
      "2/16, train_loss: 0.0444\n",
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      "16/16, train_loss: 0.0661\n",
      "17/16, train_loss: 0.0781\n",
      "epoch 540 average loss: 0.0616\n",
      "current epoch: 540 current mean dice: 0.8541 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 541/600\n",
      "1/16, train_loss: 0.0431\n",
      "2/16, train_loss: 0.0435\n",
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      "17/16, train_loss: 0.0485\n",
      "epoch 541 average loss: 0.0582\n",
      "----------\n",
      "epoch 542/600\n",
      "1/16, train_loss: 0.0530\n",
      "2/16, train_loss: 0.0476\n",
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      "epoch 542 average loss: 0.0602\n",
      "----------\n",
      "epoch 543/600\n",
      "1/16, train_loss: 0.0787\n",
      "2/16, train_loss: 0.0532\n",
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      "16/16, train_loss: 0.0527\n",
      "17/16, train_loss: 0.0446\n",
      "epoch 543 average loss: 0.0641\n",
      "----------\n",
      "epoch 544/600\n",
      "1/16, train_loss: 0.0405\n",
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      "epoch 544 average loss: 0.0631\n",
      "----------\n",
      "epoch 545/600\n",
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      "16/16, train_loss: 0.0693\n",
      "17/16, train_loss: 0.0547\n",
      "epoch 545 average loss: 0.0592\n",
      "current epoch: 545 current mean dice: 0.8690 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 546/600\n",
      "1/16, train_loss: 0.0470\n",
      "2/16, train_loss: 0.0407\n",
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      "17/16, train_loss: 0.0372\n",
      "epoch 546 average loss: 0.0586\n",
      "----------\n",
      "epoch 547/600\n",
      "1/16, train_loss: 0.0442\n",
      "2/16, train_loss: 0.0397\n",
      "3/16, train_loss: 0.0595\n",
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      "epoch 547 average loss: 0.0589\n",
      "----------\n",
      "epoch 548/600\n",
      "1/16, train_loss: 0.0427\n",
      "2/16, train_loss: 0.0422\n",
      "3/16, train_loss: 0.0634\n",
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      "17/16, train_loss: 0.0496\n",
      "epoch 548 average loss: 0.0580\n",
      "----------\n",
      "epoch 549/600\n",
      "1/16, train_loss: 0.0584\n",
      "2/16, train_loss: 0.0461\n",
      "3/16, train_loss: 0.0760\n",
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      "16/16, train_loss: 0.0661\n",
      "17/16, train_loss: 0.0685\n",
      "epoch 549 average loss: 0.0624\n",
      "----------\n",
      "epoch 550/600\n",
      "1/16, train_loss: 0.0481\n",
      "2/16, train_loss: 0.0432\n",
      "3/16, train_loss: 0.0665\n",
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      "16/16, train_loss: 0.0513\n",
      "17/16, train_loss: 0.0584\n",
      "epoch 550 average loss: 0.0601\n",
      "current epoch: 550 current mean dice: 0.8672 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 551/600\n",
      "1/16, train_loss: 0.0436\n",
      "2/16, train_loss: 0.0472\n",
      "3/16, train_loss: 0.0493\n",
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      "16/16, train_loss: 0.0705\n",
      "17/16, train_loss: 0.0402\n",
      "epoch 551 average loss: 0.0562\n",
      "----------\n",
      "epoch 552/600\n",
      "1/16, train_loss: 0.0510\n",
      "2/16, train_loss: 0.0496\n",
      "3/16, train_loss: 0.0736\n",
      "4/16, train_loss: 0.0656\n",
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      "15/16, train_loss: 0.0643\n",
      "16/16, train_loss: 0.0673\n",
      "17/16, train_loss: 0.0434\n",
      "epoch 552 average loss: 0.0588\n",
      "----------\n",
      "epoch 553/600\n",
      "1/16, train_loss: 0.0472\n",
      "2/16, train_loss: 0.0497\n",
      "3/16, train_loss: 0.0486\n",
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      "15/16, train_loss: 0.0454\n",
      "16/16, train_loss: 0.0633\n",
      "17/16, train_loss: 0.0760\n",
      "epoch 553 average loss: 0.0614\n",
      "----------\n",
      "epoch 554/600\n",
      "1/16, train_loss: 0.0559\n",
      "2/16, train_loss: 0.0521\n",
      "3/16, train_loss: 0.1320\n",
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      "11/16, train_loss: 0.0692\n",
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      "15/16, train_loss: 0.0491\n",
      "16/16, train_loss: 0.0765\n",
      "17/16, train_loss: 0.0716\n",
      "epoch 554 average loss: 0.0660\n",
      "----------\n",
      "epoch 555/600\n",
      "1/16, train_loss: 0.0424\n",
      "2/16, train_loss: 0.0509\n",
      "3/16, train_loss: 0.0741\n",
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      "12/16, train_loss: 0.0651\n",
      "13/16, train_loss: 0.0687\n",
      "14/16, train_loss: 0.0673\n",
      "15/16, train_loss: 0.0831\n",
      "16/16, train_loss: 0.0628\n",
      "17/16, train_loss: 0.0513\n",
      "epoch 555 average loss: 0.0622\n",
      "current epoch: 555 current mean dice: 0.8479 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 556/600\n",
      "1/16, train_loss: 0.0570\n",
      "2/16, train_loss: 0.0483\n",
      "3/16, train_loss: 0.0578\n",
      "4/16, train_loss: 0.0694\n",
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      "11/16, train_loss: 0.0847\n",
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      "15/16, train_loss: 0.0696\n",
      "16/16, train_loss: 0.0719\n",
      "17/16, train_loss: 0.0557\n",
      "epoch 556 average loss: 0.0616\n",
      "----------\n",
      "epoch 557/600\n",
      "1/16, train_loss: 0.0525\n",
      "2/16, train_loss: 0.0854\n",
      "3/16, train_loss: 0.0507\n",
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      "15/16, train_loss: 0.0464\n",
      "16/16, train_loss: 0.0603\n",
      "17/16, train_loss: 0.0659\n",
      "epoch 557 average loss: 0.0629\n",
      "----------\n",
      "epoch 558/600\n",
      "1/16, train_loss: 0.0501\n",
      "2/16, train_loss: 0.0489\n",
      "3/16, train_loss: 0.0484\n",
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      "16/16, train_loss: 0.0673\n",
      "17/16, train_loss: 0.0504\n",
      "epoch 558 average loss: 0.0635\n",
      "----------\n",
      "epoch 559/600\n",
      "1/16, train_loss: 0.0465\n",
      "2/16, train_loss: 0.0418\n",
      "3/16, train_loss: 0.0477\n",
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      "16/16, train_loss: 0.0620\n",
      "17/16, train_loss: 0.0738\n",
      "epoch 559 average loss: 0.0590\n",
      "----------\n",
      "epoch 560/600\n",
      "1/16, train_loss: 0.0882\n",
      "2/16, train_loss: 0.0576\n",
      "3/16, train_loss: 0.0622\n",
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      "16/16, train_loss: 0.0576\n",
      "17/16, train_loss: 0.0581\n",
      "epoch 560 average loss: 0.0634\n",
      "current epoch: 560 current mean dice: 0.8592 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 561/600\n",
      "1/16, train_loss: 0.0477\n",
      "2/16, train_loss: 0.0445\n",
      "3/16, train_loss: 0.0613\n",
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      "15/16, train_loss: 0.0534\n",
      "16/16, train_loss: 0.0654\n",
      "17/16, train_loss: 0.0489\n",
      "epoch 561 average loss: 0.0562\n",
      "----------\n",
      "epoch 562/600\n",
      "1/16, train_loss: 0.0406\n",
      "2/16, train_loss: 0.0514\n",
      "3/16, train_loss: 0.0752\n",
      "4/16, train_loss: 0.0656\n",
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      "15/16, train_loss: 0.0533\n",
      "16/16, train_loss: 0.0520\n",
      "17/16, train_loss: 0.0442\n",
      "epoch 562 average loss: 0.0595\n",
      "----------\n",
      "epoch 563/600\n",
      "1/16, train_loss: 0.0583\n",
      "2/16, train_loss: 0.0462\n",
      "3/16, train_loss: 0.0594\n",
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      "16/16, train_loss: 0.0661\n",
      "17/16, train_loss: 0.0603\n",
      "epoch 563 average loss: 0.0597\n",
      "----------\n",
      "epoch 564/600\n",
      "1/16, train_loss: 0.0542\n",
      "2/16, train_loss: 0.0470\n",
      "3/16, train_loss: 0.0971\n",
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      "16/16, train_loss: 0.0825\n",
      "17/16, train_loss: 0.0574\n",
      "epoch 564 average loss: 0.0668\n",
      "----------\n",
      "epoch 565/600\n",
      "1/16, train_loss: 0.0433\n",
      "2/16, train_loss: 0.0470\n",
      "3/16, train_loss: 0.0557\n",
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      "11/16, train_loss: 0.0586\n",
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      "16/16, train_loss: 0.0637\n",
      "17/16, train_loss: 0.0506\n",
      "epoch 565 average loss: 0.0574\n",
      "current epoch: 565 current mean dice: 0.8632 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 566/600\n",
      "1/16, train_loss: 0.0439\n",
      "2/16, train_loss: 0.0597\n",
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      "16/16, train_loss: 0.0530\n",
      "17/16, train_loss: 0.0377\n",
      "epoch 566 average loss: 0.0571\n",
      "----------\n",
      "epoch 567/600\n",
      "1/16, train_loss: 0.0415\n",
      "2/16, train_loss: 0.0484\n",
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      "epoch 567 average loss: 0.0631\n",
      "----------\n",
      "epoch 568/600\n",
      "1/16, train_loss: 0.0471\n",
      "2/16, train_loss: 0.0406\n",
      "3/16, train_loss: 0.0737\n",
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      "epoch 568 average loss: 0.0614\n",
      "----------\n",
      "epoch 569/600\n",
      "1/16, train_loss: 0.0446\n",
      "2/16, train_loss: 0.0522\n",
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      "epoch 569 average loss: 0.0589\n",
      "----------\n",
      "epoch 570/600\n",
      "1/16, train_loss: 0.0523\n",
      "2/16, train_loss: 0.0595\n",
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      "16/16, train_loss: 0.0608\n",
      "17/16, train_loss: 0.0464\n",
      "epoch 570 average loss: 0.0624\n",
      "current epoch: 570 current mean dice: 0.8445 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 571/600\n",
      "1/16, train_loss: 0.0493\n",
      "2/16, train_loss: 0.0472\n",
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      "17/16, train_loss: 0.0279\n",
      "epoch 571 average loss: 0.0578\n",
      "----------\n",
      "epoch 572/600\n",
      "1/16, train_loss: 0.0613\n",
      "2/16, train_loss: 0.0357\n",
      "3/16, train_loss: 0.0475\n",
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      "15/16, train_loss: 0.0529\n",
      "16/16, train_loss: 0.0542\n",
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      "epoch 572 average loss: 0.0593\n",
      "----------\n",
      "epoch 573/600\n",
      "1/16, train_loss: 0.0517\n",
      "2/16, train_loss: 0.0456\n",
      "3/16, train_loss: 0.0606\n",
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      "11/16, train_loss: 0.0862\n",
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      "16/16, train_loss: 0.0567\n",
      "17/16, train_loss: 0.0616\n",
      "epoch 573 average loss: 0.0686\n",
      "----------\n",
      "epoch 574/600\n",
      "1/16, train_loss: 0.0463\n",
      "2/16, train_loss: 0.0424\n",
      "3/16, train_loss: 0.0833\n",
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      "16/16, train_loss: 0.0880\n",
      "17/16, train_loss: 0.0412\n",
      "epoch 574 average loss: 0.0632\n",
      "----------\n",
      "epoch 575/600\n",
      "1/16, train_loss: 0.0409\n",
      "2/16, train_loss: 0.0491\n",
      "3/16, train_loss: 0.0885\n",
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      "15/16, train_loss: 0.0554\n",
      "16/16, train_loss: 0.0677\n",
      "17/16, train_loss: 0.0570\n",
      "epoch 575 average loss: 0.0591\n",
      "current epoch: 575 current mean dice: 0.8703 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 576/600\n",
      "1/16, train_loss: 0.0381\n",
      "2/16, train_loss: 0.0476\n",
      "3/16, train_loss: 0.0642\n",
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      "16/16, train_loss: 0.0721\n",
      "17/16, train_loss: 0.0700\n",
      "epoch 576 average loss: 0.0609\n",
      "----------\n",
      "epoch 577/600\n",
      "1/16, train_loss: 0.0465\n",
      "2/16, train_loss: 0.0488\n",
      "3/16, train_loss: 0.0608\n",
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      "11/16, train_loss: 0.0672\n",
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      "14/16, train_loss: 0.0636\n",
      "15/16, train_loss: 0.0471\n",
      "16/16, train_loss: 0.0659\n",
      "17/16, train_loss: 0.1055\n",
      "epoch 577 average loss: 0.0602\n",
      "----------\n",
      "epoch 578/600\n",
      "1/16, train_loss: 0.0508\n",
      "2/16, train_loss: 0.0475\n",
      "3/16, train_loss: 0.0844\n",
      "4/16, train_loss: 0.0591\n",
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      "6/16, train_loss: 0.0702\n",
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      "12/16, train_loss: 0.0671\n",
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      "15/16, train_loss: 0.0612\n",
      "16/16, train_loss: 0.0548\n",
      "17/16, train_loss: 0.0651\n",
      "epoch 578 average loss: 0.0657\n",
      "----------\n",
      "epoch 579/600\n",
      "1/16, train_loss: 0.0493\n",
      "2/16, train_loss: 0.0460\n",
      "3/16, train_loss: 0.0667\n",
      "4/16, train_loss: 0.0731\n",
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      "14/16, train_loss: 0.0593\n",
      "15/16, train_loss: 0.0488\n",
      "16/16, train_loss: 0.0606\n",
      "17/16, train_loss: 0.0571\n",
      "epoch 579 average loss: 0.0646\n",
      "----------\n",
      "epoch 580/600\n",
      "1/16, train_loss: 0.0540\n",
      "2/16, train_loss: 0.0444\n",
      "3/16, train_loss: 0.0900\n",
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      "14/16, train_loss: 0.0545\n",
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      "16/16, train_loss: 0.0629\n",
      "17/16, train_loss: 0.0551\n",
      "epoch 580 average loss: 0.0624\n",
      "current epoch: 580 current mean dice: 0.8475 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 581/600\n",
      "1/16, train_loss: 0.0556\n",
      "2/16, train_loss: 0.0458\n",
      "3/16, train_loss: 0.0688\n",
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      "16/16, train_loss: 0.0615\n",
      "17/16, train_loss: 0.1345\n",
      "epoch 581 average loss: 0.0666\n",
      "----------\n",
      "epoch 582/600\n",
      "1/16, train_loss: 0.0416\n",
      "2/16, train_loss: 0.0417\n",
      "3/16, train_loss: 0.0544\n",
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      "14/16, train_loss: 0.0545\n",
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      "16/16, train_loss: 0.0644\n",
      "17/16, train_loss: 0.0447\n",
      "epoch 582 average loss: 0.0589\n",
      "----------\n",
      "epoch 583/600\n",
      "1/16, train_loss: 0.0565\n",
      "2/16, train_loss: 0.0499\n",
      "3/16, train_loss: 0.0406\n",
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      "16/16, train_loss: 0.0550\n",
      "17/16, train_loss: 0.0445\n",
      "epoch 583 average loss: 0.0586\n",
      "----------\n",
      "epoch 584/600\n",
      "1/16, train_loss: 0.0481\n",
      "2/16, train_loss: 0.0464\n",
      "3/16, train_loss: 0.0632\n",
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      "15/16, train_loss: 0.0511\n",
      "16/16, train_loss: 0.0766\n",
      "17/16, train_loss: 0.0582\n",
      "epoch 584 average loss: 0.0611\n",
      "----------\n",
      "epoch 585/600\n",
      "1/16, train_loss: 0.0453\n",
      "2/16, train_loss: 0.0508\n",
      "3/16, train_loss: 0.0635\n",
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      "11/16, train_loss: 0.0911\n",
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      "15/16, train_loss: 0.0520\n",
      "16/16, train_loss: 0.0622\n",
      "17/16, train_loss: 0.0642\n",
      "epoch 585 average loss: 0.0576\n",
      "current epoch: 585 current mean dice: 0.8702 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 586/600\n",
      "1/16, train_loss: 0.0421\n",
      "2/16, train_loss: 0.0551\n",
      "3/16, train_loss: 0.0689\n",
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      "16/16, train_loss: 0.0569\n",
      "17/16, train_loss: 0.0507\n",
      "epoch 586 average loss: 0.0586\n",
      "----------\n",
      "epoch 587/600\n",
      "1/16, train_loss: 0.0510\n",
      "2/16, train_loss: 0.0435\n",
      "3/16, train_loss: 0.0512\n",
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      "16/16, train_loss: 0.0503\n",
      "17/16, train_loss: 0.0426\n",
      "epoch 587 average loss: 0.0545\n",
      "----------\n",
      "epoch 588/600\n",
      "1/16, train_loss: 0.0530\n",
      "2/16, train_loss: 0.0441\n",
      "3/16, train_loss: 0.0499\n",
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      "16/16, train_loss: 0.0666\n",
      "17/16, train_loss: 0.0573\n",
      "epoch 588 average loss: 0.0565\n",
      "----------\n",
      "epoch 589/600\n",
      "1/16, train_loss: 0.0387\n",
      "2/16, train_loss: 0.0440\n",
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      "14/16, train_loss: 0.0602\n",
      "15/16, train_loss: 0.0497\n",
      "16/16, train_loss: 0.0574\n",
      "17/16, train_loss: 0.0504\n",
      "epoch 589 average loss: 0.0570\n",
      "----------\n",
      "epoch 590/600\n",
      "1/16, train_loss: 0.0557\n",
      "2/16, train_loss: 0.0455\n",
      "3/16, train_loss: 0.0665\n",
      "4/16, train_loss: 0.0620\n",
      "5/16, train_loss: 0.0722\n",
      "6/16, train_loss: 0.0543\n",
      "7/16, train_loss: 0.0652\n",
      "8/16, train_loss: 0.0665\n",
      "9/16, train_loss: 0.0654\n",
      "10/16, train_loss: 0.0733\n",
      "11/16, train_loss: 0.0735\n",
      "12/16, train_loss: 0.0892\n",
      "13/16, train_loss: 0.0656\n",
      "14/16, train_loss: 0.0592\n",
      "15/16, train_loss: 0.0529\n",
      "16/16, train_loss: 0.0680\n",
      "17/16, train_loss: 0.0389\n",
      "epoch 590 average loss: 0.0632\n",
      "current epoch: 590 current mean dice: 0.8640 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 591/600\n",
      "1/16, train_loss: 0.0524\n",
      "2/16, train_loss: 0.0555\n",
      "3/16, train_loss: 0.0580\n",
      "4/16, train_loss: 0.0658\n",
      "5/16, train_loss: 0.0484\n",
      "6/16, train_loss: 0.0632\n",
      "7/16, train_loss: 0.0555\n",
      "8/16, train_loss: 0.0541\n",
      "9/16, train_loss: 0.0671\n",
      "10/16, train_loss: 0.0568\n",
      "11/16, train_loss: 0.0734\n",
      "12/16, train_loss: 0.0699\n",
      "13/16, train_loss: 0.0658\n",
      "14/16, train_loss: 0.0604\n",
      "15/16, train_loss: 0.0574\n",
      "16/16, train_loss: 0.0545\n",
      "17/16, train_loss: 0.0440\n",
      "epoch 591 average loss: 0.0590\n",
      "----------\n",
      "epoch 592/600\n",
      "1/16, train_loss: 0.0570\n",
      "2/16, train_loss: 0.0441\n",
      "3/16, train_loss: 0.0730\n",
      "4/16, train_loss: 0.0538\n",
      "5/16, train_loss: 0.0519\n",
      "6/16, train_loss: 0.0674\n",
      "7/16, train_loss: 0.0521\n",
      "8/16, train_loss: 0.0647\n",
      "9/16, train_loss: 0.0482\n",
      "10/16, train_loss: 0.0612\n",
      "11/16, train_loss: 0.0629\n",
      "12/16, train_loss: 0.0724\n",
      "13/16, train_loss: 0.0666\n",
      "14/16, train_loss: 0.0630\n",
      "15/16, train_loss: 0.0530\n",
      "16/16, train_loss: 0.1111\n",
      "17/16, train_loss: 0.0625\n",
      "epoch 592 average loss: 0.0627\n",
      "----------\n",
      "epoch 593/600\n",
      "1/16, train_loss: 0.0481\n",
      "2/16, train_loss: 0.0520\n",
      "3/16, train_loss: 0.0786\n",
      "4/16, train_loss: 0.0555\n",
      "5/16, train_loss: 0.0783\n",
      "6/16, train_loss: 0.0690\n",
      "7/16, train_loss: 0.0492\n",
      "8/16, train_loss: 0.0605\n",
      "9/16, train_loss: 0.0496\n",
      "10/16, train_loss: 0.0982\n",
      "11/16, train_loss: 0.0635\n",
      "12/16, train_loss: 0.0852\n",
      "13/16, train_loss: 0.0765\n",
      "14/16, train_loss: 0.0569\n",
      "15/16, train_loss: 0.0571\n",
      "16/16, train_loss: 0.0788\n",
      "17/16, train_loss: 0.0575\n",
      "epoch 593 average loss: 0.0656\n",
      "----------\n",
      "epoch 594/600\n",
      "1/16, train_loss: 0.0400\n",
      "2/16, train_loss: 0.0412\n",
      "3/16, train_loss: 0.0546\n",
      "4/16, train_loss: 0.0580\n",
      "5/16, train_loss: 0.0461\n",
      "6/16, train_loss: 0.0531\n",
      "7/16, train_loss: 0.0646\n",
      "8/16, train_loss: 0.0471\n",
      "9/16, train_loss: 0.0384\n",
      "10/16, train_loss: 0.0632\n",
      "11/16, train_loss: 0.0593\n",
      "12/16, train_loss: 0.0766\n",
      "13/16, train_loss: 0.0701\n",
      "14/16, train_loss: 0.0679\n",
      "15/16, train_loss: 0.0512\n",
      "16/16, train_loss: 0.0724\n",
      "17/16, train_loss: 0.0563\n",
      "epoch 594 average loss: 0.0565\n",
      "----------\n",
      "epoch 595/600\n",
      "1/16, train_loss: 0.0546\n",
      "2/16, train_loss: 0.0558\n",
      "3/16, train_loss: 0.0667\n",
      "4/16, train_loss: 0.0583\n",
      "5/16, train_loss: 0.0605\n",
      "6/16, train_loss: 0.0585\n",
      "7/16, train_loss: 0.0567\n",
      "8/16, train_loss: 0.0587\n",
      "9/16, train_loss: 0.0561\n",
      "10/16, train_loss: 0.0499\n",
      "11/16, train_loss: 0.0645\n",
      "12/16, train_loss: 0.0655\n",
      "13/16, train_loss: 0.0656\n",
      "14/16, train_loss: 0.0576\n",
      "15/16, train_loss: 0.0666\n",
      "16/16, train_loss: 0.0543\n",
      "17/16, train_loss: 0.0686\n",
      "epoch 595 average loss: 0.0599\n",
      "current epoch: 595 current mean dice: 0.8717 \n",
      "best mean dice: 0.8783  at epoch: 510\n",
      "----------\n",
      "epoch 596/600\n",
      "1/16, train_loss: 0.0562\n",
      "2/16, train_loss: 0.0471\n",
      "3/16, train_loss: 0.0723\n",
      "4/16, train_loss: 0.0684\n",
      "5/16, train_loss: 0.0542\n",
      "6/16, train_loss: 0.0618\n",
      "7/16, train_loss: 0.0726\n",
      "8/16, train_loss: 0.0569\n",
      "9/16, train_loss: 0.0695\n",
      "10/16, train_loss: 0.0640\n",
      "11/16, train_loss: 0.0520\n",
      "12/16, train_loss: 0.0635\n",
      "13/16, train_loss: 0.0641\n",
      "14/16, train_loss: 0.0567\n",
      "15/16, train_loss: 0.0428\n",
      "16/16, train_loss: 0.0548\n",
      "17/16, train_loss: 0.0623\n",
      "epoch 596 average loss: 0.0600\n",
      "----------\n",
      "epoch 597/600\n",
      "1/16, train_loss: 0.0462\n",
      "2/16, train_loss: 0.0489\n",
      "3/16, train_loss: 0.0626\n",
      "4/16, train_loss: 0.0694\n",
      "5/16, train_loss: 0.0596\n",
      "6/16, train_loss: 0.0844\n",
      "7/16, train_loss: 0.0559\n",
      "8/16, train_loss: 0.0500\n",
      "9/16, train_loss: 0.0595\n",
      "10/16, train_loss: 0.0669\n",
      "11/16, train_loss: 0.0687\n",
      "12/16, train_loss: 0.0587\n",
      "13/16, train_loss: 0.0745\n",
      "14/16, train_loss: 0.0625\n",
      "15/16, train_loss: 0.0503\n",
      "16/16, train_loss: 0.0750\n",
      "17/16, train_loss: 0.0442\n",
      "epoch 597 average loss: 0.0610\n",
      "----------\n",
      "epoch 598/600\n",
      "1/16, train_loss: 0.0420\n",
      "2/16, train_loss: 0.0640\n",
      "3/16, train_loss: 0.0645\n",
      "4/16, train_loss: 0.0555\n",
      "5/16, train_loss: 0.0503\n",
      "6/16, train_loss: 0.0701\n",
      "7/16, train_loss: 0.0636\n",
      "8/16, train_loss: 0.0651\n",
      "9/16, train_loss: 0.0503\n",
      "10/16, train_loss: 0.0532\n",
      "11/16, train_loss: 0.0687\n",
      "12/16, train_loss: 0.0783\n",
      "13/16, train_loss: 0.0592\n",
      "14/16, train_loss: 0.0820\n",
      "15/16, train_loss: 0.0521\n",
      "16/16, train_loss: 0.0555\n",
      "17/16, train_loss: 0.0635\n",
      "epoch 598 average loss: 0.0611\n",
      "----------\n",
      "epoch 599/600\n",
      "1/16, train_loss: 0.0467\n",
      "2/16, train_loss: 0.0535\n",
      "3/16, train_loss: 0.0897\n",
      "4/16, train_loss: 0.0601\n",
      "5/16, train_loss: 0.0563\n",
      "6/16, train_loss: 0.0580\n",
      "7/16, train_loss: 0.0625\n",
      "8/16, train_loss: 0.0648\n",
      "9/16, train_loss: 0.0561\n",
      "10/16, train_loss: 0.0601\n",
      "11/16, train_loss: 0.0614\n",
      "12/16, train_loss: 0.0669\n",
      "13/16, train_loss: 0.0634\n",
      "14/16, train_loss: 0.0712\n",
      "15/16, train_loss: 0.0566\n",
      "16/16, train_loss: 0.0720\n",
      "17/16, train_loss: 0.0582\n",
      "epoch 599 average loss: 0.0622\n",
      "----------\n",
      "epoch 600/600\n",
      "1/16, train_loss: 0.0449\n",
      "2/16, train_loss: 0.0455\n",
      "3/16, train_loss: 0.0526\n",
      "4/16, train_loss: 0.0505\n",
      "5/16, train_loss: 0.0571\n",
      "6/16, train_loss: 0.0483\n",
      "7/16, train_loss: 0.0650\n",
      "8/16, train_loss: 0.0528\n",
      "9/16, train_loss: 0.0956\n",
      "10/16, train_loss: 0.0714\n",
      "11/16, train_loss: 0.0634\n",
      "12/16, train_loss: 0.0756\n",
      "13/16, train_loss: 0.0642\n",
      "14/16, train_loss: 0.0584\n",
      "15/16, train_loss: 0.0541\n",
      "16/16, train_loss: 0.0731\n",
      "17/16, train_loss: 0.0583\n",
      "epoch 600 average loss: 0.0606\n",
      "current epoch: 600 current mean dice: 0.8660 \n",
      "best mean dice: 0.8783  at epoch: 510\n"
     ]
    }
   ],
   "source": [
    "root_dir = '/mnt/datawow/liuyiquan/trained_model'\n",
    "max_epochs = 600\n",
    "val_interval = 5\n",
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "post_pred = Compose([AsDiscrete(argmax=True, to_onehot=num_classes)])\n",
    "post_label = Compose([AsDiscrete(to_onehot=num_classes)])\n",
    "roi_size = (8,128,128)\n",
    "slice_to_track = 30\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    print(\"-\" * 10)\n",
    "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "    for batch_data in train_loader:\n",
    "        step += 1\n",
    "        inputs, labels = (\n",
    "            batch_data[\"image\"].to(device),\n",
    "            batch_data[\"label\"].to(device),\n",
    "        )\n",
    "        # print(inputs.shape, labels.shape)\n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        loss = loss_function(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size}, \" f\"train_loss: {loss.item():.4f}\")\n",
    "        # track batch loss metric\n",
    "        # aim_run.track(loss.item(), name=\"batch_loss\", context={\"type\": loss_type})\n",
    "\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append(epoch_loss)\n",
    "\n",
    "    # track epoch loss metric\n",
    "    # aim_run.track(epoch_loss, name=\"epoch_loss\", context={\"type\": loss_type})\n",
    "\n",
    "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        # if (epoch + 1) % val_interval * 2 == 0:\n",
    "        #     # track model params and gradients\n",
    "        #     track_params_dists(model, aim_run)\n",
    "        #     # THIS SEGMENT TAKES RELATIVELY LONG (Advise Against it)\n",
    "        #     track_gradients_dists(model, aim_run)\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for index, val_data in enumerate(val_loader):\n",
    "                val_inputs, val_labels = (\n",
    "                    val_data[\"image\"].to(device),\n",
    "                    val_data[\"label\"].to(device),\n",
    "                )\n",
    "\n",
    "                sw_batch_size = 4\n",
    "                val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "                # val_outputs = model(val_inputs)\n",
    "                # tracking input, label and output images with Aim\n",
    "                # output = torch.argmax(val_outputs, dim=1)[0, :, :, slice_to_track].float()\n",
    "\n",
    "                # aim_run.track(\n",
    "                #     aim.Image(val_inputs[0, 0, :, :, slice_to_track], caption=f\"Input Image: {index}\"),\n",
    "                #     name=\"validation\",\n",
    "                #     context={\"type\": \"input\"},\n",
    "                # )\n",
    "                # aim_run.track(\n",
    "                #     aim.Image(val_labels[0, 0, :, :, slice_to_track], caption=f\"Label Image: {index}\"),\n",
    "                #     name=\"validation\",\n",
    "                #     context={\"type\": \"label\"},\n",
    "                # )\n",
    "                # aim_run.track(\n",
    "                #     aim.Image(output, caption=f\"Predicted Label: {index}\"),\n",
    "                #     name=\"predictions\",\n",
    "                #     context={\"type\": \"labels\"},\n",
    "                # )\n",
    "\n",
    "                val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "                val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "                # compute metric for current iteration\n",
    "                dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "            # aggregate the final mean dice result\n",
    "            metric = dice_metric.aggregate().item()\n",
    "            # track val metric\n",
    "            # aim_run.track(metric, name=\"val_metric\", context={\"type\": loss_type})\n",
    "\n",
    "            # reset the status for next validation round\n",
    "            dice_metric.reset()\n",
    "\n",
    "            metric_values.append(metric)\n",
    "            if metric > best_metric:\n",
    "                best_metric = metric\n",
    "                best_metric_epoch = epoch + 1\n",
    "                torch.save(model.state_dict(), os.path.join(root_dir, f\"{model_name}_best_metric_model.pth\"))\n",
    "\n",
    "                best_model_log_message = f\"saved new best metric model at the {epoch+1}th epoch\"\n",
    "                # aim_run.track(aim.Text(best_model_log_message), name=\"best_model_log_message\", epoch=epoch + 1)\n",
    "                print(best_model_log_message)\n",
    "\n",
    "            message1 = f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "            message2 = f\"\\nbest mean dice: {best_metric:.4f} \"\n",
    "            message3 = f\"at epoch: {best_metric_epoch}\"\n",
    "\n",
    "            # aim_run.track(aim.Text(message1 + \"\\n\" + message2 + message3), name=\"epoch_summary\", epoch=epoch + 1)\n",
    "            print(message1, message2, message3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0026daf6-3365-465f-b57b-1392c48f3f64",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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